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Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely "CnD". The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm's ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals. [ABSTRACT FROM AUTHOR]

The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)—an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information. [ABSTRACT FROM AUTHOR]

Fully automated registration algorithms are prone to getting trapped in solutions corresponding to local minima of their objective functions, leading to errors that are easy to detect but challenging to correct. Traditional solutions often involve iterative parameter tuning, data preprocessing and preregistering, and multiple algorithm reruns-an approach that is both time-consuming and does not guarantee satisfactory results. Therefore, for tasks where registration accuracy is more important than speed, it is appropriate to explore alternative, user-assisted registration strategies. In such tasks, finding and correcting errors in automated registration is often more time-consuming than directly integrating user input during the registration process. Therefore, this study evaluates a user-assisted approach for accurate nonrigid registration of images and traces. By leveraging the corresponding sets of fiducial points provided by the user to guide the registration, the algorithm computes an optimal nonrigid transformation that combines linear and nonlinear components. Our findings demonstrate that the registration accuracy of this approach improves consistently with the increased complexity of the linear transformation and as more fiducial points are provided. As a result, accuracy sufficient for many biomedical applications can be achieved within minutes, requiring only a small number of user-provided fiducial points.

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

The purpose is to estimate the effectiveness of electrocardiograms during resting and active participation by the differentiation between the electrical activity of the heart while standing and sitting in a resting state. The concern is to identify the electrocardiogram parameters that did not show significant changes within these positions. The electrocardiogram parameters can be considered to be a standard marker for medically compromised patients. The electrocardiogram is recorded in the standing and sitting positions focusing on healthy participants using standard electrode placement of lead-I. Combined lead-I patterns (camel-hump or ST-segment prolongation) are usually seen in neurologic injury or hypothermia patients. The pairwise comparisons of a year data are about 454,400 cycles of sitting and 493,470 cycles of standing data. Thus, it is essential to quantify the nature and magnitude of changes seen in the electrocardiogram with a change of posture from sitting to standing in a healthy individual. This makes the findings of electrocardiogram analysis in this paper interesting in which some parameters (i.e., camel-hump patterns in lead-I) are helpful for clinical interpretations and could be suggestive of neurologic injury.

This paper presents a system for electrocardiogram (ECG) delineation and arrhythmia classification. The proposed system consists of a front-end integrated circuit, a delineation algorithm implemented on an FPGA board, and an arrhythmia classification algorithm. The front-end circuit applies a ternary second-order Delta modulator to measure the slope variation of the input analog ECG signal. The circuit converts the analog inputs into a pulse density modulated bitstream, whose pulse density is proportional to the slope variation of the input analog signal regardless of the instantaneous amplitude. The front-end chip can detect the minimum slope variation of 3.2 mV/ms2 within a 3 ms timing error. The front-end integrated circuit was fabricated with a 180 nm CMOS process occupying a 0.25 mm2 area with a 151 nW power consumption at the sampling rate of 1 kS/s. Based on the slope variation obtained from the front-end circuit, a delineation algorithm is designed to detect fiducial points in the ECG waveform. The delineation algorithm was tested on a Spartan-6 FPGA. The delineation system can detect the intervals, slopes, and morphology of the QRS/PT waves and form a feature set that contains 22 features. Based on these features, a rotate linear kernel support vector machine (SVM) is applied for patient-specific arrhythmia classification of the ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), and heartbeats originating in sinus node. The performance of the proposed system is comparable to the recently published methods while providing a promising solution for the low-complexity implementation of future wearable ECG monitoring systems. [ABSTRACT FROM AUTHOR]

• Neuronavigation is required to perform rTMS deep cortical targets such as the posterior superior insula (PSI). • The Fast-PSI was precise when compared to the gold-standard neuronavigation method, being easy and fast to perform. • The Fast-PSI identifies the orthogonal scalp projection of the PSI based on craniometry in a neuronavigation-free approach. [ABSTRACT FROM AUTHOR]

Accurately detected human body fiducial points provide an easy and efficient method for human body posture analysis and the extraction of anthropometric parameters. In the proposed work, an efficient algorithm for automated and accurate detection of fiducial points is developed for both the frontal and the lateral images. An algorithm for automatic human body segmentation of the frontal image is also developed using automatically detected set of primary fiducial points. Additional fiducial points are obtained by applying peak and valley algorithm on the silhouettes of each segment. The detection accuracy of the automatically detected fiducial points is calculated by comparing their locations with the manually marked fiducial points. The proposed algorithm is tested on 45 subjects including both male and female genders and variable Body Mass Indexes. In most cases, the algorithm successfully detects seventy fiducial points for each subject in the testing set. A quantitative analysis of the error in the position of the detected fiducial points shows that the algorithm performs better than the state-of-the-art algorithms found in the existing literature. In the evaluation of the algorithm, the percentage accuracy of the detected fiducial points is calculated and it is observed that the proposed algorithm performs better for the majority of the fiducial points. [ABSTRACT FROM AUTHOR]

This paper proposes a face recognition system with the capacity of achieving high recognition rates, even if the face image presents significant occlusion, using a wireframe model and a Facefit tool that acquires a coordinate system with fiducial points that characterize the facial parts. Thereby, the occlusions are emulated by the selection of points that represent four cases of partial blockage of the face such as: occlusion in eyes area, occlusion in the area of mouth and occlusion in left and right part of face in a separately way. Computer simulations show that the proposed system provides recognition rates higher than 95% in each case, providing also stability to the identification system. Thus, the wireframe model presents a robust coordinate system that even in complex cases, as data loss, is able to obtain a fairly good recognition rate. [ABSTRACT FROM PUBLISHER]

Purpose: Improvement in real-time electrocardiogram (ECG) interpretation is still needed, especially for QT estimation. This paper proposes a fast algorithm for ECG feature recognition, based on locating turning points in the waveform gradient.Methods: The algorithm places the fiducial point at the maximal value of a probabilistic decision function, assessing line intervals of best fit before and after the point and the point location relative to R-wave peaks already found.Results: Fiducial points were successfully located for the 30 heartbeats annotated by a cardiologist of all 10 normal sinus rhythm records from the PhysioNet QT Database. For a given subject, the algorithm's QT estimation had superior repeatability, with intrasubject QT standard deviation just 5.42ms, 60% lower than the cardiologist's 13.57ms. Initial tests suggest immunity to noise of standard deviation up to about 9% of the signal, depending on noise type.Conclusions: The proposed algorithm is fast to calculate and noise-tolerant, and has shown improved repeatability in its QT estimation compared to a cardiologist. [ABSTRACT FROM AUTHOR]

A methodology for makeup invariant robust face recognition based on features from accelerated segment test and Eigen vectors is proposed. Makeup and cosmetic changes in face have been a major cause of security breaches since long time. It is not only difficult for human eyes to catch an imposter but also an equally daunting task for a face recognition system to correctly identify an individual owing to changes brought about in face due to makeup. As a crucial pre-processing step, the face is first divided into various segments centered on the eyes, nose, lips and cheeks. FAST algorithm is then applied over the face images. The features thus derived from the facial image act as the fiducial points for that face. Thereafter principal component analysis is applied over the set of fiducial points in each segment of every face image present in the data sets in order to compute the Eigen vectors and the Eigen values. The resultant principal component which is the Eigen vector with the highest Eigen value yields the direction of the features in that segment. The principal components thus obtained using fiducial points generated from FAST in each segment of the test and the training data are compared in order to get the best match or no match. [ABSTRACT FROM AUTHOR]

One of the most effective ways of expressing emotion is through facial expressions. This work proposes and discusses a geometrical descriptor based on the calculation of distances from coordinates of facial fiducial points, which are used as features for training support vector machines (SVM) to classify emotions. Three data sets are studied and six basic emotions are considered in our experiments. In comparison to other approaches available in the literature, the results obtained with our geometrical descriptor demonstrated to be very competitive, achieving high classification F-score rates. Additionally, we evaluate whether the combination of our geometrical descriptor with an appearance feature, the Gabor filter, allows emotions to be even more distinguishable for the classifier. The result is positive for two out of three data sets. Finally, to simulate in-the-wild scenarios, an active appearance model (AAM) is trained to position the fiducial points on the correct facial locations, instead of using the ones provided by the data sets. As the fitting error is considered acceptable, the former experiments are also conducted with the new data generated by the AAM. The results show a small drop on the F-score values when compared to the data originally provided by the data sets,but are still satisfactory. [ABSTRACT FROM AUTHOR]

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays. [ABSTRACT FROM AUTHOR]

A liberal amount of software applications are in market for generating a sketch out of an image, the vice-versa though is unacquainted. Whereas such an implementation will prove to be purposive to the crime investigation departments. Such a youthful approach for generating an image from a sketch is suggested in this paper by following a process of, breaking down the sketch into constituent or component of face, matching or comparing these features with the available database, selecting the best match followed by registering or pasting these image components on a blank face image, performing filtering algorithm in order to perform smoothening of image. [ABSTRACT FROM AUTHOR]

Functional brain imaging techniques require accurate co-registration to anatomical images to precisely identify the areas being activated. Many of them, including diffuse optical imaging, rely on scalp-placed recording sensors. Fiducial alignment is an effective and rapid method for co-registering scalp sensors onto anatomy, but is quite sensitive to placement errors. Surface Euclidean distance minimization using the Levenberq-Marquart algorithm (LMA) has been shown to be very accurate when based on good initial guesses, such as precise fiducial alignment, but its accuracy drops substantially with fiducial placement errors. Here we compared fiducial and LMA co-registration methods to a new procedure, the iterative closest point-to-plane (ICP2P) method, using simulated and real data. An advantage of ICP2P is that it eliminates the need to identify fiducials and is, therefore, entirely automatic. We show that, typically, ICP2P is as accurate as fiducial-based LMA, but is less sensitive to initial placement errors. However, ICP2P is more sensitive to spatially correlated noise in the description of the head surface. Hence, the best technique for co-registration depends on the type of data available to describe the scalp and the surface defined by the recording sensors. Under optimal conditions, co-registration error using surface-fitting procedures can be reduced to ~3 mm. [ABSTRACT FROM AUTHOR]

Purpose: Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy have become standard treatments but are more sensitive to anatomical variations than 3D conformal techniques. To correct for inter- and intrafraction anatomical variations, fast and easy to implement methods are needed. Here, the authors propose a full dosimetric IMRT correction that finds a compromise in-between basic repositioning (the current clinical practice) and full replanning. It simplifies replanning by avoiding a recontouring step and a full dose calculation. It surpasses repositioning by updating the preoptimized fluence and monitor units (MU) using a limited number of fiducial points and a pretreatment (CB)CT. To adapt the fluence the fiducial points were projected in the beam's eye view (BEV). To adapt the MUs, point dose calculation towards the same fiducial points were performed. The proposed method is intrinsically fast and robust, and simple to understand for operators, because of the use of only four fiducial points and the beam data based point dose calculations. Methods: To perform our dosimetric adaptation, two fluence corrections in the BEV are combined with two MU correction steps along the beam's path. (1) A transformation of the fluence map such that it is realigned with the current target geometry. (2) A correction for an unintended scaling of the penumbra margin when the treatment beams scale to the current target size. (3) A correction for the target depth relative to the body contour and (4) a correction for the target distance to the source. The impact of the correction strategy and its individual components was evaluated by simulations on a virtual prostate phantom. This heterogeneous reference phantom was systematically subjected to population based prostate transformations to simulate interfraction variations. Additionally, a patient example illustrated the clinical practice. The correction strategy was evaluated using both dosimetric (CTV mean dose, conformity index) and clinical (tumor control probability, and normal tissue complication probability) measures. Results: Based on the current experiments, the intended target dose and tumor control probability could be assured by the proposed method (TCP = TCPintended). Additionally, the conformity index error was more than halved compared to the current clinical practice (ΔCI95% from 40% to 16%) resulting in improved organ at risk protection. All the individual correction steps had an added value to the full correction. Conclusions: A limited number of fiducial points (no organ contours required) and an inroom (CB)CT are sufficient to perform a full dosimetric correction for IMRT plans. In the presence of interfraction variation, the corrected plans show superior dose distributions compared to our current clinical practice. [ABSTRACT FROM AUTHOR]

The aim of an automated Electrocardiogram (ECG) delineation system is the reliable detection of the characteristic waveforms and determination of peaks and limits of individual QRS-complex, P- and T-waves. In this paper, a classical statistical pattern recognition algorithm characterized with high accuracy and stability, i.e., K-Nearest Neighbour (KNN) has been proposed for locating the fiducial points along with their waveform boundaries in ECG signals. First, the QRS-complex along with its onset and offset points of each beat is detected from the ECG signal. After that P- and T-wave, relative to each QRS-complex along with their onset and offset points, are then identified using this algorithm. The feature extraction is done using the gradient of the ECG signals. The performance of the proposed algorithm has been evaluated on two standard manually annotated databases, (i) CSE and (ii) QT, and also on ECG data acquired using BIOPAC®MP100 system in laboratory settings. The results in terms of accuracy, i.e., 92.8% for CSE database obtained, clearly indicate a high degree of agreement with the manual annotations made by the referees of CSE dataset-3. Further, the delineation results of the CSE and QT database are compared with the accepted tolerances as recommended by the CSE working party. The results for ECG records acquired using the BIOPAC®MP100 system, in terms of QRS duration, heart rate, QT-interval, P-wave duration and PR-interval using KNN algorithm have also been computed. [ABSTRACT FROM AUTHOR]

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively. [ABSTRACT FROM AUTHOR]

We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and occlusion than prior ones. We show excellent performance on real-world face datasets such as Labeled Faces in the Wild (LFW) and a new Labeled Face Parts in the Wild (LFPW) and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset. [ABSTRACT FROM PUBLISHER]

Detection and delineation of QRS-complexes, P and T-waves, are important issues in the analysis and interpretation of Electrocardiogram (ECG) signals. In this paper, a classifier motivated from statistical learning theory, i.e., Support Vector Machine (SVM), has been explored for detection and delineation of these wave components. Digital filtering techniques are used to remove interference present in ECG signal. The feature extraction is done using a modified definition of slope of the ECG signals. The performance of the proposed algorithm is validated using ECG recordings from dataset-3 of the CSE multi-lead measurement library. The results in terms of accuracy, i.e., 94.4%, obtained clearly indicate a high degree of agreement with the manual annotations made by the referees of CSE dataset-3. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a phase-space based algorithm applying the Euclidian distance measure enabling detection of heartbeats and characteristic (fiducial) points from a single-lead electrocardiogram (ECG) signal. It extends the QRS detection in the phase space by detecting the P and T fiducial points. The algorithm is derived by reconstructing the ECG signals in a two-dimensional (2D) phase space according to the delay method and utilizes geometrical properties of the reconstructed phase portrait of the signal in the phase space for the heartbeat and fiducial-point detection. It uses adaptive thresholding and the Euclidian distance measure between the signal points in the phase portrait as an alternative to the phase-portrait area calculation (Lee et al., 2002 ). It was verified with the QT Database (2011; ) and its performance was assessed using sensitivity (Se) and the positive predictive value (PPV). Results for the proposed algorithm are 99.06%, 99.75% and 99.66% for Se and 94.87%, 99.75% and 99.66% for PPV for the P points, heartbeats and T points, respectively. [Copyright &y& Elsevier]

• A model for the simulation of photoplethysmographic signals, with varying pulse rate variability information, is proposed. • The selection of interbeat intervals detection algorithm and fiducial points alters the results of pulse rate variability when measured from photoplethysmographic signals. In general, it was found that more reliable results can be obtained when onset-related fiducial points and algorithms were used. • The sampling rate used to acquire the photoplethysmographic signals needs to be carefully selected for the assessment of pulse rate variability, according to the indices that are to be analyzed. However, sampling rates lower than 256 Hz are sufficient for assessing most time-domain and Poincaré plot indices. Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content. PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests. From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz. The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application. The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal. [ABSTRACT FROM AUTHOR]

A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining the precise position of the waveforms of an ECG signal with baseline wander is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and the unknown drift. A model-based approach handles these complications, therefore a method based on this concept has been developed and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model. Simulation results show that the proposed method has an average sensitivity of 99.58%, average detection accuracy of 99.64%, and specificity of 100%. [ABSTRACT FROM AUTHOR]