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Objective To investigate the potential of EEG multiscale entropy and complexity as biomarkers in infantile spasms. Methods We collected EEG data retrospectively from 16 newly diagnosed patients, 16 age- and gender-matched healthy controls, and 15 drug-resistant patients. The multiscale entropy (MSE) and total EEG complexity before anti-epileptic drug (AED) treatment, before adrenocorticotropic hormone (ACTH) treatment, 14 days after ACTH therapy, and after 6 months of follow-up were calculated. Results The total EEG complexity of 16 newly diagnosed infantile spasms patients was lower than the 16 healthy controls (median [IQR]: 351.5 [323.1–388.1] vs 461.6 [407.7–583.4]). The total EEG complexity before treatment was higher in the six patients with good response to AED than the 10 patients without response (median [IQR]: 410.0 [388.1–475.0] vs 344.5 [319.6–352.0]). The total EEG complexity before and after 14-days of ACTH therapy was not different between 13 ACTH therapy responders and nine non-responders. After 6-months follow-up, the total EEG complexity of ACTH therapy responders were higher than non-responders (median [IQR]: 598.5 [517.4–623.3] vs 448.6 [347.1–536.3]). Conclusions The total EEG complexity before AED and 6 months after ACTH are associated with spasm-freedom. Significance The total EEG complexity is a potential biomarker to predict and monitor the treatment effect in infantile spasms.

In this paper, a new model known as YOLO-v5 is initiated to detect defects in PCB. In the past many models and different approaches have been implemented in the quality inspection for detection of defect in PCBs. This algorithm is specifically selected due to its efficiency, accuracy and speed. It is well known that the traditional YOLO models (YOLO, YOLO-v2, YOLO-v3, YOLO-v4 and Tiny-YOLO-v2) are the state-of-the-art in artificial intelligence industry. In electronics industry, the PCB is the core and the most basic component of any electronic product. PCB is almost used in each and every electronic product that we use in our daily life not only for commercial purposes, but also used in sensitive applications such defense and space exploration. These PCB should be inspected and quality checked to detect any kind of defects during the manufacturing process. Most of the electronic industries are focused on the quality of their product, a small error during manufacture or quality inspection of the electronic products such as PCB leads to a catastrophic end. Therefore, there is a huge revolution going on in the manufacturing industry where the object detection method like YOLO-v5 is a game changer for many industries such as electronic industries. Ministry of Science and Technology, Taiwan

According to a recently conducted survey on surgical complication mortality rate, 47% of such cases are due to anesthetics overdose. This indicates that there is an urgent need to moderate the level of anesthesia. Recently deep learning (DL) methods have played a major role in estimating the depth of Anesthesia (DOA) of patients and has played an essential role in control anesthesia overdose. In this paper, Electroencephalography (EEG) signals have been used for the prediction of DOA. EEG signals are very complex signals which may require months of training and advanced signal processing techniques. It is a point of debate whether DL methods are an improvement over the already existing traditional EEG signal processing approaches. One of the DL algorithms is Convolutional neural network (CNN) which is very popular algorithm for object recognition and is widely growing its applications in processing hierarchy in the human visual system. In this paper, various decomposition methods have been used for extracting the features EEG signal. After acquiring the necessary signals values in image format, several CNN models have been deployed for classification of DOA depending upon their Bispectral Index (BIS) and the signal quality index (SQI). The EEG signals were converted into the frequency domain using and Empirical Mode Decomposition (EMD), and Ensemble Empirical Mode Decomposition (EEMD). However, because of the inter mode mixing observed in EMD method; EEMD have been utilized for this study. The developed CNN models were used to predict the DOA based on the EEG spectrum images without the use of handcrafted features which provides intuitive mapping with high efficiency and reliability. The best trained model gives an accuracy of 83.2%. Hence, this provides further scope and research which can be carried out in the domain of visual mapping of DOA using EEG signals and DL methods. Ministry of Science and Technology, Taiwan

Currently, no device is available for the rapid screening of ventilator-associated pneumonia (VAP) at an early stage. Accordingly, we propose the design of an offline gas detection system to monitor and detect metabolites of pneumonia at an early stage. An electronic nose (e-nose) with 28 metal oxide semiconductor gas sensors was developed for predicting the presence of infection after patients have been intubated in the intensive care unit. The effectiveness of VAP identification was verified using clinical data. A total of 40 patients were included in this study, of whom 20 were infected with Pseudomonas aeruginosa and the remaining were uninfected. The results revealed that good accuracy rates of 0.9208% ± 0.0302% and 0.8547% ± 0.0214% were achieved by support vector machine and artificial neural network models, respectively. This study provides a simple, low-cost solution for the rapid screening of VAP at an early stage.

Objective: Heart rate variability (HRV) reflects autonomous nervous system disturbance and is used for seizure prediction. The aim of this study was to develop a real-time, continuous physiological medical signal data acquisition system in seizure detection for intensive care unit (ICU). Methods: This prospective study was conducted in National Taiwan University Hospital from August 2018 to October 2019. This study included 20 patients who (a) had a sustained return of spontaneous circulation following out-of-hospital cardiac arrest, (b) were over 18 years old, (c) and were admitted to the emergency ICU for post-cardiac-arrest care. One-lead electrocardiography and bilateral two-channel electroencephalography recorders were synchronically used to conduct measurements for a maximum of 72 hours. The recorded data were wirelessly real-time transmitted by a proxy transmitting module through an access point and a local gateway. A system with a novel algorithm processed the signals and conducted feature extraction and supervised learning for seizure detection. Results: A total of 89 nonseizure and 83 seizure events were detected by the system. Seizure occurred in two-thirds of the patients assessed by intensivists and neurologists. Four HRV parameters, namely standard deviation of normal-to-normal R-wave intervals, high frequency, low frequency-high frequency ratio, and sample entropy, were determined as potential features for identifying seizures. The sensitivity and specificity of the developed system were 0.74 and 0.81, respectively, and the positive predictive value was 0.82. Conclusion: The developed system can be used to identify seizure events through HRV features. Significance: The current study achieved real-time seizure detection and overcame previous limitations on continuity and accessibility.

Data Availability Statement: This study utilizes the publicly available dataset, from https://physionet.org, accessed on 14 January 2021. Copyright: © 2021 by the authors. This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson's linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system-the systolic blood pressure (SBP) and diastolic blood pressures (DBP)-the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system-the systolic and diastolic pressures-the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal. Funding: This research received no external funding.

© 2021 by the authors. Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, Electrodermal activity, respiratory, blood volume pulse, skin tempera-ture) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or de-vices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Ministry of Science and Technology (MOST) of Taiwan (grant number: MOST 107-2221-E-155-009-MY2).

Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart disease prevention is one of the most important tasks of any health care system. Despite the high popularity electrocardiography, superior automatic electrocardiography (ECG) signal analysis methods are required. The aim of this research was to design a new deep learning method for effectively classifying arrhythmia by using 2-second segments of 2D recurrence plot images of ECG signals. In the first stage, the noise and ventricular fibrillation (VF) categories were distinguished. In the second stage, the atrial fibrillation (AF), normal, premature AF, and premature VF categories were distinguished. Models were trained and tested using ECG databases publicly available at the website of PhysioNet. The MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database, MIT-BIH Atrial Fibrillation Database, and MIT-BIH Malignant Ventricular Ectopy Database were used to compare six types of arrhythmia. Testing accuracies of up to 95.3 % ± 1.27 % and 98.41 % ± 0.11 % were achieved for arrhythmia detection in the first and second stage, respectively, after five-fold cross-validation. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating between different arrhythmia types.

© 2020 by the authors. In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32. Ministry of Science and Technology of Taiwan; Boardtek Electronics Corporation, Taiwan

One of the most challenging predictive data analysis efforts is accurate prediction of depth of anesthesia (DOA) indicators which has attracted a growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using deep convolutional neural network (CNN) for object recognition are becoming widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures, is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform (STFT) is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping. This research was financially supported by Lenovo Technology B.V. Taiwan Branch. Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342).

Cardiopulmonary resuscitation (CPR) resuscitates patients suffering from cardiac arrest. Mechanical chest compression CPR highlights the need for high CPR quality to facilitate survival and neurological recovery. However, current CPR devices cannot be used on pregnant women or infants. These devices’ long re-setup times interrupt CPR and can cause cerebral ischemia. This study designed a novel device with a crank-sliding mechanism. The polar coordinate system (r, θ, z) shortened the setup time and enabled adjustment without moving the patient. We compared our device with commercial products (e.g., LUCAS-2) by quantifying the compression pressure. Control groups for manual CPR of trained physicians and untrained citizens were recruited. We used Resusci Anne products as models. Our results indicated that our design exhibited performance similar to that of LUCAS-2 in adults (557.8 vs. 623.6 mmHg, p = 0.217) and met the current CPR standard guidelines. Notably, our device is applicable to pregnant women [565 vs. 564.5 (adults) mmHg, p = 0.987] and infants [570.8 vs. 564.5 (adults) mmHg, p = 0.801] without lowering the compression quality. The overall compression quality and stability of mechanical chest compression CPR were favorable to those of manual CPR. Our device provides an innovative prototype for the next generation of CPR facilities.

Phase-rectified signal averaging (PRSA) has been known to be a useful method to detect periodicities in non-stationary biological signals. Determination of quasi-periodicities in electroencephalogram (EEG) is a candidate for quantifying the changes of depth of anesthesia (DOA). In this paper, DOA monitoring capacity of periodicities detected using PRSA were quantified by assessing EEG signals collected from 56 patients during surgery. The method is compared to sample entropy (SampEn), detrended fluctuation analysis (DFA) and permutation entropy (PE). The performance of quasi-periodicities defined by acceleration capacity (AC) and deceleration capacity (DC) was tested using the area under the receiver operating characteristic curve (AUC) and Pearson correlation coefficient. During the surgery, a significant difference (p < 0.05) in the quasi-periodicities was observed among three different stages under general anesthesia. There is a larger mean AUC and correlation coefficient of quasi-periodicities compared to SampEn, DFA and PE using expert assessment of conscious level (EACL) and bispectral index (BIS) as the gold standard, respectively. Quasi-periodicities detected using PRSA in EEG signals are powerful monitor of DOA and perform more accurate and robust results compared to SampEn, DFA and PE. The results do provide a valuable reference to researchers in the filed of clinical applications. 10.13039/501100003711-Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by the Ministry of Science and Technology; 10.13039/501100001809-National Natural Science Foundation of China

According United States Geological Survey, Aceh is the northwestern part in Indonesia that has been affected by numerous strong earthquakes since 2004 tsunami. These earthquakes have generated massive impact to the buildings around the area, especially for the reinforced concrete based buildings. One of the most important problems to the reinforced concrete is the earthquake-generated crack. In this study, the dataset from the normal and cracked reinforce concrete are collected by taking the normal and cracked images. Several convolutional neural network models are implemented such as LeNet based models. These models are initially applied to recognize either normal or cracked conditions. Eventually, for the last stage, the localization of the crack is visualized by imposing the original images. For the localization, this study also evaluates the relatively smaller and bigger cracks. The results show the higher input image with modified LeNet generates better results compared to the basic model in superimposing the localized crack. The authors would like to express their gratitude to Universitas Syiah Kuala for financial support partly for research through the Professor's research grant No. 03 / UN11.2 / PP / PNBP / SP3 / 2019.

Summary: Heart rate variability (HRV), analyzed by non-linear methods confer prognostic values. HRV of a heart reviving from extracorporeal circulation (EC) has not been elaborated. We applied Multi-scale entropy (MSE) and Detrended fluctuation analysis (DFA) to study the HRV of patients receiving open-heart surgeries needing EC, during which the heart is standstill. Thirty-four patients were enrolled. ECG was recorded continuously. The analyses were categorized by clinical outcomes. HRV of those without major cardiovascular events (No-MACE) significantly decreased in complexity (4.92 ± 2.80 to 2.94 ± 1.87, p

This study evaluates the combination of computational fluid dynamics (CFD) and artificial intelligence (AI) algorithms in heat exchanger application. More specifically, fuzzy logic controller (FLC) is selected in the closed-loop control system. The sensor read by the FLC is based on the reading from the CFD meshing finite element method (FEM) discretization in order to solve the Navier-Stokes (N-S) equation. The controller output is the boundary condition of the CFD system (i.e heat flux). In this study, the CFD result will provide a predicted value approaching the set point. This system utilizes six rules for the controller. In order to evaluate the controller performance, several different set points are set. In addition, the genetic algorithm (GA) is set as the optimizer for the making of the sequence of the Mamdani triangular membership function. Finally, the GA is used to modify the membership function of the FLC. The total evaluation error for the FLC is 0.92 °C of the mean absolute error (MAE). Meanwhile, the GA-based FCL produces 0.78 °C of the MAE.

The emulation of human behavior for autonomous problem solving has been an interdisciplinary field of research. Generally, classical control systems are used for static environments, where external disturbances and changes in internal parameters can be fully modulated before or neglected during operation. However, classical control systems are inadequate at addressing environmental uncertainty. By contrast, autonomous systems, which were first studied in the field of control systems, can be applied in an unknown environment. This paper summarizes the state of the art autonomous systems by first discussing the definition, modeling, and system structure of autonomous systems and then providing a perspective on how autonomous systems can be integrated with advanced resources (e.g., the Internet of Things, big data, Over-the-Air, and federated learning). Finally, what comes after reaching full autonomy is briefly discussed.

One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients&rsquo, data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models&rsquo, performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 &plusmn, 0.0135 and 0.8686 &plusmn, 0.0422 accuracies, 0.9714 &plusmn, 0.0131, 0.9250 &plusmn, 0.0423 sensitivities, and 0.9288 &plusmn, 0.0306, 0.8639 &plusmn, 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 &plusmn, 0.0058 and 0.9410 &plusmn, 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.

During surgeries, the amount of used anesthetic depends on the physical conditions of the patient and is immensely critical. The conventionally used BIS Quantro machine which measures the Bispectral Index (BIS) level in order to help doctors administer anesthesia, is quite expensive. In this paper, an economic, accurate and state-of-the-art technique is presented to predict the depth of anesthesia (DoA) via advanced deep learning models using 512 Hz Electrocardiogram (ECG) and 128 Hz Photoplethysmography (PPG). The study is conducted based on signal collected from 50 patients acquired during surgery at National Taiwan University Hospital (NTUH). First, heatmaps of the ECG and PPG signals (individual and combined subplots) are generated using MATLAB by filtering 5 s windows to match the frequency of the BIS Quantro Machine which is 0.2 Hz. Then, various deep learning models comprising 5, 6, 8, 10 and 19 layered CNNs are trained using data of 40 patients and tested using the remaining 10 patients. The heatmap images of ECG and PPG are fed as inputs to the CNN models separately and using two input channels. The best accuracy achieved is 86 % which is attained using 10 layered CNN with Tensorflow backend, with combined ECG and PPG heatmaps as inputs. This study uses inexpensive signals, minimum data reconstruction, minimum memory and timing constrains to achieve a decent accuracy, and so it can be used by even small hospitals.

Cerebral autoregulation (CA) is an important mechanism for maintaining constant cerebral blood flow during changes in blood pressure. Although previous studies have shown that CA may be impaired in patients with intracerebral hemorrhage (ICH), the variability of cerebral blood flow (CBF) in response to changes in CA has not been investigated. In the present study, we recruited twelve patients presenting with acute subcortical ICH and seven non-stroke controls. The status of CA was determined by assessing Pearson’s moving correlation coefficient between arterial blood pressure and cerebral blood flow velocity (CBFV) of the middle cerebral artery (MCA). The variability of CBFV was calculated using nonlinear sample entropy analyses. The results indicated that ICH patients significantly showed poorer CA than controls, particularly localized to the MCA ipsilateral to the ICH (controls 0.17 ± 0.13 versus ICH side 0.41 ± 0.27 and non-ICH side 0.34 ± 0.26, p = 0.044 and 0.120, respectively). Moreover, the variability of CBFV in the hemisphere ipsilateral to the ICH was significantly increased in ICH patients when compared to controls (1.145 ± 0.26 versus 1.66 ± 0.193 and 1.595 ± 0.43, p

Objective To address the importance of bundle care for catheter-related infection (CRBSI) on the basis of long-term observation in a catheter-abundant cardiovascular intensive care unit (CVICU). Design Prospective longitudinal cohort study. Setting CVICU of a tertiary referring medical centre in northern Taiwan. Participants Around 1400 critically ill patients annually for 5 years in the CVICU (from January 2010 to June 2015). CRBSI bundle care has been applied ever since by a multidisciplinary team. Main outcome measures CRBSI per 1000 catheter days, bloodstream infection (BSI) per 1000 inpatient days, and catheter utilisation rates. Results From January 2010 to June 2015 (22 quarters), there were in total 45 140 inpatient days and 24 163 catheter days, with an overall central venous catheter utilisation rate of 53.5%. The duration of the indwelled catheter was 6.3±1.2 days. The beginning CRBSI rate was 7.0 per 1000 catheter days and was significantly decreased to 0.7 per 1000 catheter days (p Conclusions Through the bundle care, we successfully reduced CRBSIs. After 5 years of follow-up, we observed that the effect of bundle care was stepwise and persistent, as long as we kept working on this integrated project.

In this paper we have deployed the concept of deep learning known as convolutional neural networks (CNN) as we can realize nowadays deep learning is growing in each and every field. Deep learning is executed in each and every platform and its outcome is impressive. On the other hand, the capability and accuracy of deep learning is somehow compared with human beings. We trained CNN to classify either defective or good printed circuit board (PCB). In this experiment we have used 41,387 images, which is divided into 3 different data sets i.e. training, validation and testing. The CNN, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. Hence, deep learning via convolution neural networks has been introduced in this paper, which will eventually increase the accuracy and reduce a lot of time and consumption of skilled manpower. According to this preliminary study, we can overall achieve accuracy of above 85% and minimize the count of defective PCB classifying as good. In the near future, we hope that over 95% accuracy can be achieved by using different CNN models like VGGNET, RESNET and GOOGLENET and collecting more PCB image data in order to reduce the consumption of time, manpower and increase the accuracy in quality inspection.

Copyright © 2019 by the authors. Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.

Acute stroke commonly affects cardiac autonomic responses resulting in reduced heart rate variability (HRV). Multiscale entropy (MSE) is a novel non-linear method to quantify the complexity of HRV. This study investigated the influence of intracerebral hemorrhage (ICH) locations and intraventricular hemorrhage (IVH) on the complexity of HRV. We recruited 93 supratentorial ICH patients (male 59%, mean age 61 years), and the locations of ICH included basal ganglia (n = 40), thalamus (n = 35), and lobar (n = 18) group. Continuous one-hour electrocardiography signals were obtained from patients after admission, and the complexity index was defined as the area under the MSE curve. The complexity index was lower in lobar ICH (21.6 ± 7.9) than basal ganglia (27.9 ± 6.4) and thalamus (28.5 ± 7.2) groups. The complexity index was inversely correlated with initial stroke severity (r = −0.26), size of hematoma (r = −0.35) and ICH score (r = −0.26), especially among patients with intraventricular hemorrhage (r = −0.60, −0.60, and −0.41 respectively). A higher complexity can predict a good functional outcome (adjusted odds ratio 1.09, 95% confidence intervals 1.00–1.19) at post-stroke 3 months. In summary, more severe stroke and larger hematoma volume resulted in lower complexity of HRV. Lobar hemorrhage and IVH had great impacts on the cardiac autonomic function.

With critical importance of medical healthcare, there exist urgent needs for in-depth medical studies that can access and analyze specific physiological signals to provide theoretical support for practical clinical care. As a consequence, obtaining the valuable medical data with minimal cost and impacts on hospital work comes as the first concern of researchers. Anesthesia plays a widely recognized role in surgeries, which attracts people to undertake relevant research. In this paper, a real-time physiological medical signal data acquisition system (PMSDA) for the multi-operating room applications is proposed with high universality of the hospital practical settings and research requirements. By utilizing a wireless communication approach, it provides an easily accessible network platform for collection of physiological medical signals such as photoplethysmogram (PPG), electrocardiograph (ECG) and electroencephalogram (EEG) during the surgery. In addition, the raw data is stored on a server for safe backup and further analysis of depth of anesthesia (DoA). Results show that the PMSDA exhibits robust, high quality performance and efficiently reduces costs compared to previously manual methods and allows seamless integration into hospital environment, independent of its routine work. Overall, it provides a pragmatic and flexible surgery-data acquisition system model with low impact and resource cost applicable to research in critical and practical medical circumstances.

Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by Ministry of Science and Technology (Grant Number: MOST103-2911-I-008-001). National Natural Science Foundation of China (Grant Number: 51475342).

Background/Purpose Mannitol is commonly used in patients with increased intracranial pressure (ICP), but its effect on cerebrovascular pressure reactivity (CVPR) is uncertain. We analyzed the changes of pressure reactivity index (PRx) during the course of mannitol treatment. Methods Twenty-one patients who received mannitol treatment for increased ICP were recruited prospectively. Continuous waveforms of arterial blood pressure (ABP) and ICP were collected simultaneously for 60 minutes (10 minutes at baseline and 50 minutes since mannitol administration) during 37 events of mannitol treatment. The correlation coefficients between the mean ABP and ICP were averaged every 10 minutes and labeled as the PRx. The linear correlation of six time points of PRx in each event was calculated to represent the trend of CVPR changes. The negative slope of correlation was defined as improvement in CVPR under mannitol treatment and vice versa. Results At baseline, the average of ICP was 26.0 ± 9.1 mmHg and the values of PRx were significantly correlated with ICP ( p = 0.0044, r = 0.46). After mannitol administration, the average of ICP decreased significantly to 21.2 ± 11.1 mmHg ( p = 0.036), and CVPR improved in 59.4 % of all events. Further analysis showed that low baseline cerebral perfusion pressure was the only hemodynamic parameter significant association with the improvement of CVPR after mannitol treatment ( p = 0.039). Conclusion Despite lowering ICP, mannitol may have diverse effects on CVPR in patients with intracranial hypertension. Our study suggests that mannitol infusion may have a beneficial effect on CVPR, particularly in those with a low cerebral perfusion pressure at baseline.

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert–Huang transform (HHT). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach.

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. Cal-Comp Electronics & Communications Co., Ltd.; Kinpo Electronics, Inc. New Taipei City, Taiwan. Innovation Center for Big Data and Digital Convergence; Yuan Ze University, Taiwan.

Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.

Continuous infusion of local anesthetics in surgical wounds has been shown to be an effective technique for postoperative analgesia. To investigate the potential antimicrobial effect of continuous local anesthetic infusion, we adapted a mouse model of surgical wound infection to examine effects on antibacterial response. Forty male BALB/c mice were randomized into 2 groups. An incision wound was made over the dorsal flank and instilled with Staphylococcus aureus. An osmotic pump was then implanted to deliver either 0.9% NaCl or 2% lidocaine continuously. Each wound was cultured postoperatively at 2 days, and the colony count of S. aureus was determined. Results showed that the number of colony-forming units of S. aureus measured in wounds treated with lidocaine displayed a nearly 10-fold reduction compared to the wounds in the saline group (P=0.009). The demonstrated antibacterial activity indicates that local anesthetic infusion may play a role in prophylaxis for surgical wound infections.

Diagnosis of depth of anaesthesia (DoA) plays an important role in treatment and drug usage in the operating theatre and intensive care unit. With the flourishing development of analysis methods and monitoring devices for DoA, a small amount of physiological data had been stored and shared for further researches. In this paper, a critical care monitoring (CCM) system for DoA monitoring and analysis was designed and developed, which includes two main components: a physiologic information database (PID) and a DoA analysis subsystem. The PID, including biologic data and clinical information was constructed through a browser and server model so as to provide a safe and open platform for storage, sharing and further study of clinical anaesthesia information. In the analysis of DoA, according to our previous studies on approximate entropy, sample entropy (SampEn) and multi-scale entropy (MSE), the SampEn and MSE were integrated into the subsystem for indicating the state of patients underwent surgeries in real time because of their stability. Therefore, this CCM system not only supplies the original biological data and information collected from the operating room, but also shares our studies for improvement and innovation in the research of DoA.

EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.

In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.

Entropy-based data analysis methods have been applied to many science or engineering fields. The centre of pressure (COP) data are often dynamic, non-linear and non-stationary in nature. It is thought that it may be used to analyse COP data, which is often used for evaluating postural stability in ergonomics research. Multi-scale entropy (MSE) and empirical mode decomposition are introduced first; then, three examples are presented to demonstrate the initial applications of the proposed methods, young and elderly subjects: (1) standing with attention; (2) standing with eyes open/closed; and (3) wearing vibratory shoes. The MSE analysis results are compared with traditional COP stabilogram metrics. Based on these applications, MSE can be an effective metric for COP data analysis and other ergonomics research. The cautions of using MSE, including the non-specificity nature and individual baseline variation, are also discussed. There is a good potential for using MSE in ergonomics-related research, especiall...

This study evaluates the correlation between the intermittent blood pressure (BP) and the photoplethysmography (PPG). This study of a total of twenty-five cases is started by the partitioning of the PPG signal into a 5-minute segment. The segmented PPG is filtered by ensemble empirical mode decomposition (EEMD). The feature extraction method, multiscale entropy (MSE) is utilized for the purified signal to achieve some information. The seventy-five scale of MSE is taken into the input of the artificial neural network (ANN) modeling. The outputs of this system are the intermittent diastolic and systolic blood pressure. Originally, thousand models are created. The best model is chosen for the best single ANN model. In advanced, the ensemble artificial neural network (EANN) model is initiated to observe the testing data. The result, compared to the best single ANN model, shows that the EANN model recognizes better the testing data by producing lower mean absolute error (MAE).

Atrial fibrillation (AF) detection is crucial for stroke prevention. We investigated the potential of quantitative analyses of photoplethysmogram (PPG) waveforms to identify AF. Continuous electrocardiogram (EKG) and fingertip PPG were recorded simultaneously in acute stroke patients (n = 666) admitted to an intensive care unit. Each EKG was visually labeled as AF (n = 150, 22.5%) or non-AF. Linear and nonlinear features from the pulse interval (PIN) and peak amplitude (AMP) of PPG waveforms were extracted from the first 1, 2, and 10 min of data. Logistic regression analysis revealed six independent PPG features feasibly identifying AF rhythm, including three PIN-related (mean, mean of standard deviation, and sample entropy), and three AMP-related features (mean of the root mean square of the successive differences, sample entropy, and turning point ratio) (all p

Emotion influences human health significantly. In this pilot study, a movie clips method has been designed to induce 5 kinds of emotion states. 90-sec corresponding ECG signal have been measured in the end of video stimulus. Heart rate variability (HRV) features were extracted from ECG signal by using time-domain, frequency-domain, Poincare, and statistic analysis. Then these HRV features were used to classify different emotion states by support vectors machine (SVM). Also, we used principal component analysis (PCA) to reduce the number of extracted features. Briefly, in the classification for 2 emotion states (positive/negative) and 5 kinds of emotion states, the accuracy of 71.4%, 56.9% are reached, respectively. Compared with other studies of emotion recognition using 2 or more vital signs, the accuracy in this study is lower slightly than other studies (56.9% versus 61.6%). However, using single ECG signal or HRV features is accessible for the daily emotion monitoring. Our results showed the feasibility of daily emotion monitoring by using extracted HRV features and SVM classifier.

The purpose of this paper is to propose a modular integrating algorithm. This algorithm can let the program detect multiple arrhythmias and is very easy to add more diseases detection algorithm. Also, it can save the repeated calculations in multiple algorithms. By a real test of the program, the result is that the computing time of the integrating algorithm is 46.86% less than the sum of the computing time of all individual algorithms. And the average accuracy is also improved from 94.73% to 95.35%.

Due to 3D printing is very popular of recent years, we can use 3D printer to make anything we want, and even like the prosthetic hands. We found a foreign network platform (ENABLING THE FUTURE) in which we can download a variety of prosthetic hands' files for free. We join the Enabling the Future organization, and often talk to the members and discuss with the problem we found in the hand devices. A patient, who has a missing arm below the elbow on his left hand, noticed we have been doing the research on 3D printed prosthetic hands, so he asked us for help. In this study, we try to build his own 3D printed prosthetic hand in a low-cost but functional way, so that we can make his dream of playing the guitar come true.

Tri-axial accelerometer is a useful sensor. Most devices use it to detect the steps and human's activity. Some devices use it for digital spirit level meter. Since the first generation of iPhone was released on June 29, 2007, modern smart phones have built in accelerometers which promise to enable quantifying minute-by-minute what people do (e.g., walk or sit). However, smart phones are not suitable for detecting the walking posture. This paper uses wearable device with the tri-axial accelerometer to detect the patterns of the steps and situation of humpbacked to determine the walking postures which are divided into eight walking postures. Finally, we collect ten healthy subjects to develop our algorithm and test six healthy subjects. The proposed algorithm to determine the accurate rate of walking postures in comparison with the physiotherapist is 92.98%.

Atrial fibrillation is the most commonly confronted cardiac arrhythmia in humans. This paper is written to use sample entropy and percentage of atrial fibrillation as a measure of regularity to measure AF. To assume the percentage of AF, 25 long-term ECG recordings of human subjects with atrial fibrillation containing a total of 299 AF episodes were processed. The mean and SD of percentage breaking point in all the subjects from the MIT-BIH Atrial Fibrillation database was 0.606±0.086, and its sample entropy is 0.352±0.151. The mean and SD for sample entropy at 100% AF is 1.067±0.452. This data is used to predict the percentage of AF at a given sample entropy value. Our study concludes that the early detection of AF can be initiated by the AF already happened for 60%.

Copyright @ Wang et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. The main contribution of this paper is the use of non-invasive measurements such as electrocardiogram (ECG) and photoplethysmographic (PPG) pulse oximetry waveforms to develop a new physiological signal analysis technique for detecting blood loss during surgical operation. Urological surgery cases were considered as the control group due to its generality, and cardiac surgery as experimental group since it involves blood loss and water supply. Results show that the control group has the tendency of a reduction of the pulse transient time (PTT), and this indicates an increment in the blood flow velocity changes from slow to fast. While for the experimental group, the PTT indicates high values during blood loss, and low values during water supply. Statistical analysis shows considerable differences (i.e., P

Previous studies have indicated that heart rate variability (HRV) is considered to be one of the effective parameters for assessing autonomous nervous system activity. Therefore, HRV can be used to detect the state of patients during surgery, even for estimating depth of anaesthesia (DOA). However, the heartbeat can be affected by anaesthesia drugs, breathing rate and electric influence during surgery, which can result in inaccurate information about the patient. To contend with this problem, blood flow variability (BFV) is proposed in this paper as an indicator to monitor the patient's status when HRV is not appropriate. In our experiments, thirty patients undergoing ear, nose, and throat (ENT) surgery with different anaesthetics are used to compare the efficiency and validity between the fast Fourier transform (FFT) and the Hilbert–Huang transform (HHT). Of another thirty patients, twenty patients accepted ENT surgery, of which ten have received Atropine, while the others have not. The other ten patients who have accepted abdominal surgery with an electric knife were compared with the previously mentioned ten patients who did not receive an Atropine injection for ENT surgery. As a result, the FFT that is applied in this study was replaced with the HHT for analysing the data in a particular frequency range of sympathetic and parasympathetic divisions, because of the lesser response of the results that were analysed by FFT for intubation. Also, BFV is proven to be a useful indicator for assisting doctors to assess the state of the patients instead of HRV during the operation in comparison with HRV under drugs (i.e., Atropine and Glycopyrrolate) and diathermy effects (i.e., high frequency interference from electric knife).

The purpose of this study is to use ensembled neural networks (ENN) to model survival rate for the patients with out-of-hospital cardiac arrest (OHCA). We also use seven different sensitivity analyses to find out the important variables to establish a comprehensive and objective assessment method for the OHCA patients. After pre-filtering, we obtained 4,095 data for building this ENN model. The data have been divided into 60 % data for training, 20 % data for validation, and 20 % data for testing. The 11 inputs, including response time, on-scene time, patient transfer time, time to cardiopulmonary resuscitation (CPR), CPR on the scene, using drugs, age, gender, using airway, using automated external defibrillator (AED), and trauma type, and one output variable have been selected as ENN model structure. The results have been shown that ENN can model the OHCA patients and CPR on the scene, using drugs, on-scene time, and using airway in the top 4 of these 11 important variables after 7 different sensitivity analyses. Moreover, these four variables have also been shown significant differences when we use traditional one variable statistics analysis for these variables.

The concept of organ donation has gradually been accepted by people in recent years so the judicial brain death determination process becomes very important. Clinically, patients with irreversible apnoeic coma (IAC) will be considered legally as brain death based on a judicial process, but this process can only be applied to people who had already signed the letter of consent to organ donation. The main idea behind the proposed model is to find out an easier way to diagnose the prognosis of patients with severe head injury, and offer the medical staffs more information to determine brain death. Therefore, the technique of ensembled neural networks (ENN) based on multi-layer perceptron (MLP) network has been applied to construct the prediction model of brain death index (BDI). Ten different signals were chosen to be the input data. Using these ten parameters, medical doctors depend on their experience to score the BDI hourly values. The BDI values from medical doctors become the training target of the ANN training process and the standard index of testing process. Moreover, in order to compare the differences between doctors’ and the network's rankings for the input data, the ranking of order of precedence of each input signal is analyzed via sensitivity analysis. The results show that the 4 layers network with validation has better performance than 3 layers. For sensitivity analysis, most of the input variables’ ranking from trained model were similar to the ranking of the medical doctors except RR/RR(Set) this parameter and 4 other parameters (PS-R, PR-R, PS-L, and PR-L) are difficult to rank, even medical doctors cannot decide the ranking accurately. Using the best topology structure of MLP 10-10-5-1, the ensemble neural network could effectively predict the BDI with small errors (i.e. training error = 0.219087; validation error = 0.370485; testing error = 0.280515). In conclusion, this model can provide medical staffs a reference index to evaluate the status of IAC and brain death patients. However, more clinical data are still needed, perhaps to refine the weights of EANN, and certainly to see how widely the model is applicable.

Complexity is considered with high interest to texture analysis, such as the application of lymphoma inspection. Previously, multiscale entropy was proposed to account for the multiple time scales inherent in complex biological signals, and complexity texture descriptors were considered for content-based medical image retrieval system. However, in the past, these complexity texture descriptors were derived by linear algorithms of Fourier and wavelet transforms. In this study, we introduce a new approach of multiscale complexity (MSC) for texture analysis of lymphomas based on a nonlinear parameter of entropy. An extended algorithm of 2-D entropy was used in the calculations of complexities for the coarse-grained images of different scale factors. Furthermore, five parameters are defined to characterize 58 images of reactive lymphadenopahty and five categories of lymphomas using complexity on three scales and the changes of complexity between two successive scales. MSC analysis performed well to figure out the statistical differences of paired comparisons picked from reactive lymphadenopahty and five categories of lymphomas.

Background and objective The objective of this study was to assess whether antiemetic drugs metoclopramide and diphenhydramine, administered together as opposed to alone, can have better efficacy in preventing postoperative nausea and vomiting when added to patient-controlled morphine analgesia. Patients and methods During the period July 2007 to August 2008, 200 women scheduled for abdominal total hysterectomy were randomised to one of four postoperative, patient-controlled analgesia regimens: group 1, morphine 1 mg ml -1 ; group 2, morphine 1 mg ml -1 with metoclopramide 0.5 mg ml -1 ; group 3, morphine 1 mg ml -1 with diphenhydramine 0.6 mg ml -1 ; and group 4, morphine 1 mg ml -1 with metoclopramide 0.5 mg ml -1 and diphenhydramine 0.6 mg ml -1 . Dexamethasone 4 mg was administered to all patients in all groups after anaesthesia induction as a prophylactic antiemetic medication, and prochlorperazine 5 mg was administered by intramuscular injection as necessary as a salvage/rescue therapy. Nausea, vomiting, pruritus, level of sedation, pain and morphine consumption were compared between the four groups. Results The incidence of nausea was significantly (P< 0.05) lower in group 4 compared to the other groups. In addition, there was a significant (P= 0.006) difference in the incidence of vomiting between groups 1 and 4. Repeated measurement analysis showed that numeric rating scale scores for group 4 were significantly (P< 0.001) lower than those for the other groups. Conclusion Results of this study showed that a combination of metoclopramide with diphenhydramine in patients treated with dexamethasone at anaesthesia induction decreased postoperative nausea and vomiting compared to metoclopramide or diphenhydramine in these patients, when added to patient-controlled anaesthesia with morphine.