Search

As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an -by-1 or 1-by- array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call ''time-frequency moments singular value decomposition (TFM-SVD).'' In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

Finland is a world leader in the use of public electronic services. Continuous improvement to competencies is a prerequisite for the success of digitalisation in the service development sector. The increasing use of information technology in health and social care needs to be taken into account in the education of the health and social care sector work force. The mandate of the national SotePeda 24/7 project is to identify and define the informatics competencies required for multidisciplinary education of this sector in Finland. The project has adapted international recommendations for use in the national context. The national recommendation covers 12 areas of competency and related content. In addition to defining competencies, the project has produced a toolbox of materials for use by educators of these topics in universities that cover applied sciences and lifelong learning. The results of the project are expected to significantly improve the preparedness of graduating health and social care and related engineering and business sector students to make full use information technology, all of which benefits the national health and social welfare system. [ABSTRACT FROM AUTHOR]

The quality of software is high in medical devices due to the strict regulatory requirements and their implementation in the software development processes through the use of the IEC 62304 standard. The goal of this standard revision project was to extend the scope of the standard to all health software and also to bring the requirements of the 12 year old standard back to the state-of-the-art including provisions for cybersecurity. The joint IEC/SC62A and ISO/TC215 project team revised the standard and adapted its risk management, usability, and security requirements to serve both the medical device industry and the overall health software industry. The resulting second version of the standard has gone through a multistage global voting process to achieve a consensus of the requirements to serve both these communities. The resulting standard has potential to have a major impact on the quality of software used in health care globally. [ABSTRACT FROM AUTHOR]

The paper analyses the development of public eHealth services from 2014 to 2021 from the patients’ point of view. The merits and missing features of the eHealth services were identified with patient interviews in 2014-2015. The list of missing features was again checked against the eHealth services in 2021. The main finding was that all the features wanted by the patients had still not been implemented. The finding of this paper suggests that current Finnish public eHealth services are organizations oriented rather than patient oriented. [ABSTRACT FROM AUTHOR]

Background: Sleep spindles have different properties in different localizations in the cortex. Objectives: First main objective was to develop an amplitude-independent multi-channel spindle detection method. Secondly the method was applied to study the anteroposterior frequency differences of pure synchronous (visible bilaterally, either frontopolarly or centrally) and diffuse (visible bilaterally both frontopolarly and centrally) sleep spindles. Methods: A previously presented spindle detector based on the fuzzy reasoning principle and a level detector were combined to form a multi-channel spindle detector. Results: The spindle detector had a 76.17% true positive rate and 0.93% false-positive rate. Pure central spindles were faster and pure frontal spindles were slower than diffuse spindles measured simultaneously from both locations. Conclusions: The study of frequency relations of spindles might give new information about thalamocortical sleep spindle generating mechanisms. Copyright © 2005 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

In the present work, gender differences in sleep spindle topography were examined in 40 subjects. Their median age was 32 years (range 22–49 years). Spindles were detected from 3,306,060 s of visually scored stage 2 sleep EEG by a previously validated automatic fuzzy detector at 1-second intervals. A total of 271,168 spindles were found from the six EEG channels analyzed. Females showed a significantly higher percentage of spindles in the left frontal channel than males (Fp1-A2; p = 0.026). To confirm that this difference was gender and not age related, the subjects were divided into two age groups. No significant differences in spindle activity of the frontal channels were found between the groups. However, the interindividual spindle variability seemed to be at least as large as that stemming from gender.Copyright © 2002 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Abstract: An automatic method for the classification of wakefulness and sleep stages SREM, S1, S2 and SWS was developed based on our two previous studies. The method is based on a two-channel electro-oculography (EOG) referenced to the left mastoid (M1). Synchronous electroencephalographic (EEG) activity in S2 and SWS was detected by calculating cross-correlation and peak-to-peak amplitude difference in the 0.5–6Hz band between the two EOG channels. An automatic slow eye-movement (SEM) estimation was used to indicate wakefulness, SREM and S1. Beta power 18–30Hz and alpha power 8–12Hz was also used for wakefulness detection. Synchronous 1.5–6Hz EEG activity and absence of large eye movements was used for S1 separation from SREM. Simple smoothing rules were also applied. Sleep EEG, EOG and EMG were recorded from 265 subjects. The system was tuned using data from 132 training subjects and then applied to data from 131 validation subjects that were different to the training subjects. Cohen''s Kappa between the visual and the developed new automatic scoring in separating 30s wakefulness, SREM, S1, S2 and SWS epochs was substantial 0.62 with epoch by epoch agreement of 72%. With automatic subject specific alpha thresholds for offline applications results improved to 0.63 and 73%. The automatic method can be further developed and applied for ambulatory sleep recordings by using only four disposable, self-adhesive and self-applicable electrodes. [Copyright &y& Elsevier]

Abstract: An automatic method was developed for detecting unintentional sleep onset. The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. An automatic estimation of slow eye movements (SEM) was developed and used as the main criterion to separate sleep stage 1 (S1) from wakefulness. Additionally synchronous electroencephalographic (EEG) activity of sleep stages 1 and 2 was detected by calculating cross-correlation and amplitude difference in the 1.5–6Hz theta band between the two EOG channels. Alpha power 8–12Hz and beta power 18–30Hz were used to determine wakefulness. Unintentional sleep onsets were studied using data from four separate maintenance of wakefulness test (MWT) sessions of 228 subjects. The automatic scoring of 30s sleep onset epochs using only EOG was compared to standard visual sleep stage scoring. The optimal detection thresholds were derived using data from 114 subjects and then applied to the data from different 114 subjects. Cohen''s Kappa between the visual and the new automatic scoring system in separating wakefulness and sleep was substantial (0.67) with epoch by epoch agreement of 98%. The sleep epoch detection sensitivity was 70% and specificity 99%. The results are provided with a 1s delay for each 30s epoch. The developed method has to be tested in field applications. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive self-applicable electrodes. [Copyright &y& Elsevier]

Abstract: An automatic method was developed for detecting slow wave sleep (SWS). The automatic method is based on a two-channel electro-oculography (EOG) with left mastoid (M1) as reference. Synchronous electroencephalographic (EEG) activity was detected by calculating cross-correlation between the two EOG channels by using 0.5–6Hz band. An amplitude criterion was used for detecting slow waves and beta power 18–30Hz was used to exclude artefacts. The automatic scoring was compared to a standard visual sleep scoring based on EOG, central EEG and submental EMG. Sleep EEG and EOG were recorded from 265 subjects. The optimal cross-correlation, amplitude and beta thresholds were derived using data from 133 training subjects and then applied to the data from different 132 validation subjects. Results were most sensitive to the changes in the amplitude criteria. Cohen''s Kappa between the visual and the new developed automatic scoring in separating non-SWS and SWS was substantial (0.70) with epoch-by-epoch agreement of 93%. SWS epoch detection sensitivity was 75% and specificity was 96%. Also the total amount of slow waves, slow wave time (SWT), was estimated. The advantage of the automatic method is that it could be applied during online recordings using only four disposable self-adhesive electrodes. [Copyright &y& Elsevier]

The heart disease diagnosing (HDD) system consists of a sensitive movement EMFi™-film 1 [1] EMFi is a registered trademark of Emfit Ltd. sensor installed under the upholstery of a chair. Whilst a man rests on the chair, this sensor which is sensitive to force gives us a single electrical signal containing components reflective of cardiac-ballistocardiogram (BCG), respiratory, and body movements related motion. Among different measurements of body activities, BCG has the interesting property that no electrodes are needed to be attached to the body during recording, suitable to evaluate man heart condition in any place such as home, car, or his office. This paper describes briefly our developed HDD system and especially a combined intelligent signal processing method to detect, extract features and finally cluster BCG cycles for assisting medical doctors to diagnose heart diseases of person under test. Indeed, it is a fully automatic system which is not very sensitive to the BCG latency as well as non-linear disturbance. It uses high resolution Biorthogonal wavelet transforms to extract essential BCG features and to cluster those using artificial neural networks (ANNs). Some evaluations using recordings from normal young, normal old and abnormal old volunteers indicated that our combined method is reliable and has high performance. [Copyright &y& Elsevier]

Abstract: In this work, topographic differences in computational sleep depth between healthy controls and obstructive sleep apnoea syndrome (OSAS) patients have been examined. Sleep depth estimation was based on continuous monitoring of the mean frequency of the EEG. During the experiments, all-night sleep EEG recordings of carefully age and gender matched sets of 16 healthy controls and 16 OSAS patients were compared on six electrode locations (Fp1-M2, Fp2-M1, C3-M2, C4-M1, O1-M2, and O2-M1). To optimise the diagnostic ability of the method, we examined the influence of 45 sets of adjustable analysis parameters on the ability of the method to show differences in computational sleep depth between the diagnostic groups. The results show clearly that although the visual scores for a set of epochs are the same for both clinical groups, computational sleep depth measure still shows deeper local sleep for healthy controls, both during NREM and REM sleep. Although the best achievable performance in different sleep stages is reached in different EEG derivations and with different parameter values, computation of sleep depth with 1-s output resolution in non-overlapping segments of 2s (400 samples) with maximum analysis band frequency of 20.5Hz and 51-point moving median smoothing on Fp2-M1 or O1-M2 leads to near-optimal performance in deep sleep or wakefulness/light sleep, respectively. [Copyright &y& Elsevier]

Abstract: Accurate analysis of EEG sleep spindle frequency is challenging. The frequency content of true sleep spindles is not known. Therefore, simulated spindle activity was studied in the present work. Five types of simulated test signals were designed, all containing a dominant spindle represented by a 13-Hz sine wave as such or with a waxing and waning pattern accompanied by a secondary spindle activity in three test signals. Background EEG was included in four test signals, modeled either as small additional sinusoids across the spindle frequency range or as filtered Gaussian noise segments. The purpose of this study was to investigate how accurately the dominant spindle frequency of 13Hz could be resolved with different methods in the presence of the interfering waveforms. A matching pursuit (MP) based approach, discrete Fourier transform (DFT) with Hanning windowing with and without zero padding, Hankel total least squares (HTLS) and wavelet methods were compared in the analyses. MP method provided best overall performance, followed closely by DFT with zero padding. Comparative studies like this are important to decide the method of choice in clinical sleep EEG analysis. [Copyright &y& Elsevier]

Abstract: Sleep apnea syndrome is known to disturb sleep. The purpose of the present work was to study spindle frequency in apnea patients. All-night sleep EEG recordings of 15 apnea patients and 15 control subjects with median ages of 47 and 46 years, respectively, were studied. A previously presented and validated multi-channel spindle analysis method was applied for automatic detection and frequency analysis of bilateral frontopolar and central spindles. Bilateral frontopolar spindles of apnea patients were found to show lower frequencies on the left hemisphere than on the right. Such an inter-hemispheric spindle frequency difference in apnea patients is a novel finding. It could be that the hypoxias and hypercapnias caused by apneic episodes result in local disruption in the regulation of sleep in the frontal lobes. [Copyright &y& Elsevier]

Background: The lack of an international standard for assessing and communicating health app quality and the lack of consensus about what makes a high-quality health app negatively affect the uptake of such apps. At the request of the European Commission, the international Standard Development Organizations (SDOs), European Committee for Standardization, International Organization for Standardization, and International Electrotechnical Commission have joined forces to develop a technical specification (TS) for assessing the quality and reliability of health and wellness apps., Objective: This study aimed to create a useful, globally applicable, trustworthy, and usable framework to assess health app quality., Methods: A 2-round Delphi technique with 83 experts from 6 continents (predominantly Europe) participating in one (n=42, 51%) or both (n=41, 49%) rounds was used to achieve consensus on a framework for assessing health app quality. Aims included identifying the maximum 100 requirement questions for the uptake of apps that do or do not qualify as medical devices. The draft assessment framework was built on 26 existing frameworks, the principles of stringent legislation, and input from 20 core experts. A follow-up survey with 28 respondents informed a scoring mechanism for the questions. After subsequent alignment with related standards, the quality assessment framework was tested and fine-tuned with manufacturers of 11 COVID-19 symptom apps. National mirror committees from the 52 countries that participated in the SDO technical committees were invited to comment on 4 working drafts and subsequently vote on the TS., Results: The final quality assessment framework includes 81 questions, 67 (83%) of which impact the scores of 4 overarching quality aspects. After testing with people with low health literacy, these aspects were phrased as "Healthy and safe," "Easy to use," "Secure data," and "Robust build." The scoring mechanism enables communication of the quality assessment results in a health app quality score and label, alongside a detailed report. Unstructured interviews with stakeholders revealed that evidence and third-party assessment are needed for health app uptake. The manufacturers considered the time needed to complete the assessment and gather evidence (2-4 days) acceptable. Publication of CEN-ISO/TS 82304-2:2021 Health software - Part 2: Health and wellness apps - Quality and reliability was approved in May 2021 in a nearly unanimous vote by 34 national SDOs, including 6 of the 10 most populous countries worldwide., Conclusions: A useful and usable international standard for health app quality assessment was developed. Its quality, approval rate, and early use provide proof of its potential to become the trusted, commonly used global framework. The framework will help manufacturers enhance and efficiently demonstrate the quality of health apps, consumers, and health care professionals to make informed decisions on health apps. It will also help insurers to make reimbursement decisions on health apps., (©Petra Hoogendoorn, Anke Versluis, Sanne van Kampen, Charles McCay, Matt Leahy, Marlou Bijlsma, Stefano Bonacina, Tobias Bonten, Marie-José Bonthuis, Anouk Butterlin, Koen Cobbaert, Thea Duijnhoven, Cynthia Hallensleben, Stuart Harrison, Mark Hastenteufel, Terhi Holappa, Ben Kokx, Birgit Morlion, Norbert Pauli, Frank Ploeg, Mark Salmon, Kyma Schnoor, Mary Sharp, Pier Angelo Sottile, Alpo Värri, Patricia Williams, Georg Heidenreich, Nicholas Oughtibridge, Robert Stegwee, Niels H Chavannes. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.01.2023.)

Background: In this study, investigating the effects of mobile phone radiation on test animals, eleven pigs were anaesthetised to the level where burst-suppression pattern appears in the electroencephalogram (EEG). At this level of anaesthesia both human subjects and animals show high sensitivity to external stimuli which produce EEG bursts during suppression. The burst-suppression phenomenon represents a nonlinear control system, where low-amplitude EEG abruptly switches to very high amplitude bursts. This switching can be triggered by very minor stimuli and the phenomenon has been described as hypersensitivity. To test if also radio frequency (RF) stimulation can trigger this nonlinear control, the animals were exposed to pulse modulated signal of a GSM mobile phone at 890 MHz. In the first phase of the experiment electromagnetic field (EMF) stimulation was randomly switched on and off and the relation between EEG bursts and EMF stimulation onsets and endpoints were studied. In the second phase a continuous RF stimulation at 31 W/kg was applied for 10 minutes. The ECG, the EEG, and the subcutaneous temperature were recorded., Results: No correlation between the exposure and the EEG burst occurrences was observed in phase I measurements. No significant changes were observed in the EEG activity of the pigs during phase II measurements although several EEG signal analysis methods were applied. The temperature measured subcutaneously from the pigs' head increased by 1.6 degrees C and the heart rate by 14.2 bpm on the average during the 10 min exposure periods., Conclusion: The hypothesis that RF radiation would produce sensory stimulation of somatosensory, auditory or visual system or directly affect the brain so as to produce EEG bursts during suppression was not confirmed.

Standard sleep stage classification is based on visual analysis of central (usually also frontal and occipital) EEG, two-channel EOG, and submental EMG signals. The process is complex, using multiple electrodes, and is usually based on relatively high (200-500 Hz) sampling rates. Also at least 12 bit analog to digital conversion is recommended (with 16 bit storage) resulting in total bit rate of at least 12.8 kbit/s. This is not a problem for in-house laboratory sleep studies, but in the case of online wireless self-applicable ambulatory sleep studies, lower complexity and lower bit rates are preferred. In this study we further developed earlier single channel facial EMG/EOG/EEG-based automatic sleep stage classification. An algorithm with a simple decision tree separated 30 s epochs into wakefulness, SREM, S1/S2 and SWS using 18-45 Hz beta power and 0.5-6 Hz amplitude. Improvements included low complexity recursive digital filtering. We also evaluated the effects of a reduced sampling rate, reduced number of quantization steps and reduced dynamic range on the sleep data of 132 training and 131 testing subjects. With the studied algorithm, it was possible to reduce the sampling rate to 50 Hz (having a low pass filter at 90 Hz), and the dynamic range to 244 microV, with an 8 bit resolution resulting in a bit rate of 0.4 kbit/s. Facial electrodes and a low bit rate enables the use of smaller devices for sleep stage classification in home environments.

Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low weight single-channel Alive Monitor. In the validation study, subjects attached the disposable EOG electrodes themselves at home. In separating the four stages total agreement (and Cohen's Kappa) in the training data set was 74% (0.59), in the testing data set 73% (0.59) and in the validation data set 74% (0.59). Self-applicable electro-oculography with only two facial electrodes was found to provide reasonable sleep stage information.

The most commonly applied unobtrusive sleep monitoring method is actigraphy, the measurement of body limb movements. In spite of its wide clinical acceptance, actigraphy has a low specificity for sleep detection leaving room for novel approaches of unobtrusive sleep monitoring. The present study compared sleep detection by a novel single channel electro-oculography (EOG) method and three activity monitors, with the golden standard of polysomnographic sleep analysis as a reference. With standard actigraphy (Actiwatch placed at the left wrist) sleep detection specificity and sensitivity were 42% and 95%. With the Alive Monitor attached on the same wrist, activity-based sleep detection specificity and sensitivity were 40% and 97%. With another Alive Monitor placed over the sternum sleep detection specificity and sensitivity were 21% and 99%. With two self-applied EOG electrodes combined with automatic sleep detection analysis, specificity and sensitivity were 72% and 96%. The results confirm low specificity of actigraphic sleep estimates, and demonstrate that the novel single-channel EOG method provides a substantial improvement in specificity.

Removal of electrocardiographic (ECG) artifacts of QRS complexes from a single channel electroencephalography (EEG) and electro-oculography (EOG) can be problematic especially when no reference ECG signal is available. This study examined a simple estimation method excluding the possible QRS part of the EOG trace before spectrum estimation. The method was tested using a simple sleep classifier based on 0.5-30 Hz mean frequency of single channel sleep EOG, with the left EOG electrode referenced to the left mastoid (EOG L-M1). When QRS peaks were automatically excluded from the least square (LS) mean frequency estimation the average optimal mean frequency threshold decreased from 9.3 Hz to 8.8 Hz and agreement and Cohen's Kappa increased respectively from 89% to 90% and from 0.44 to 0.50 when compared to the traditional spectral estimation.

Although various investigators have suggested algorithms for the automatic detection of eye movements during sleep, objective comparisons of the proposed methods have previously been difficult due to different recording arrangements of different investigators. In this study the results of five eye movement detection algorithms applied to the same data were compared to visually scored data. The percentages of true and false detections are given for various threshold levels in rapid and slow eye movement detections. The methods gave best results when they were used with the same electrode montage they were designed for but the performance decreased when other montages were used. Subtracting the cross-talk of EEG delta activity improved the correctness of eye movement detections.