Biometric and Emotion Identification: An ECG Compression Based Method

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

Accurate heart rate measurement during work is required for many industrial hygiene and ergonomics situations. The purpose of this investigation was to determine the validity of heart rate measurements obtained by a simple, lightweight, commercially available wrist-worn heart rate monitor (HRM) during work (cycle exercise) sessions conducted in the laboratory and also during the particularly challenging work environment of space flight. Three different comparisons were made. The first compared HRM data to simultaneous electrocardiogram (ECG) recordings of varying heart rates that were generated by an ECG simulator. The second compared HRM data to ECG recordings collected during work sessions of 14 subjects in the laboratory. Finally, ECG downlink and HRM data were compared in four astronauts who performed cycle exercise during space flight. The data were analyzed using regression techniques. The results were that the HRM recorded virtually identical heart rates compared with ECG recordings for the data set generated by an ECG simulator. The regression equation for the relationship between ECG versus HRM heart rate data during work in the laboratory was: ECG HR = 0.99 x (HRM) + 0.82 (r2 = 0.99). Finally, the agreement between ECG downlink data and HRM data during space flight was also very high, with the regression equation being: Downlink ECG HR = 1.05 x (HRM) -5.71 (r2 = 0.99). The results of this study indicate that the HRM provides accurate data and may be used to reliably obtain valid data regarding heart rate responses during work.

In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust.

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and colored Gaussian noises to visually inspected clean ECG recordings, and studying the SNR and morphology of the filter outputs. The results of the study demonstrate superior results compared with conventional ECG denoising approaches such as bandpass filtering, adaptive filtering, and wavelet denoising, over a wide range of ECG SNRs. The method is also successfully evaluated on real nonstationary muscle artifact. This method may therefore serve as an effective framework for the model-based filtering of noisy ECG recordings.

Arrhythmia is not uncommon in the etiology of syncope which mimics epilepsy. Data about the epilepsy induced vagal tonus abnormalities have being increasingly reported. So we aimed to evaluate what a neurologist may gain by a simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recording in the patients who underwent EEG testing due to prediagnosis of epilepsy. We retrospectively evaluated and detected ECG abnormalities in 68 (18%) of 376 patients who underwent EEG testing. A minimum of 20 of minutes artifact-free recording were required for each patient. Standard 1-channel ECG was simultaneously recorded in conjunction with the EEG. In all, 28% of females and 14% of males had ECG abnormalities. Females (mean age 49 years, range 18-88 years) were older compared with the male group (mean age 28 years, range 16-83 years). Atrial fibrillation was more frequent in female group whereas bradycardia and respiratory sinus arrhythmia was higher in male group. One case had been detected a critical asystole indicating sick sinus syndrome in the female group and treated with a pacemaker implantation in the following period. Simultaneous ECG recording in conjunction with EEG testing is a clinical prerequisite to detect and to clarify the coexisting ECG and EEG abnormalities and their clinical relevance. Potentially rare lethal causes of syncope that mimic seizure or those that could cause resistance to antiepileptic therapy could effectively be distinguished by detecting ECG abnormalities coinciding with the signs and abnormalities during EEG recording.

Studies on retrospective electrocardiogram (ECG) recordings of patients during cardiac arrest have shown significant changes in heart rate variability (HRV) indices prior to the onset of cardiac arrhythmia. The early detection of these changes in HRV indices increases the chances for a successful medical intervention by increasing the response time window. A portable, handheld remote ECG monitor designed in this research detects the QRS complex and calculates short-term HRV indices in real-time. The QRS detection of the ECG recordings of subjects from the MIT-Arrhythmia database yielded a mean sensitivity of 99.34% and a specificity of 99.31%. ECG recordings from normal subjects and subjects with congestive heart failure were used to identify the differences in HRV indices. An increase in heart rate, high-frequency spectral power (HFP), total spectral power, the ratio of HFP to low-frequency spectral power (LFP), and a decrease in root mean square sum of RR differences were observed. No difference was found on comparison of the standard deviation of normal to normal interval between adjacent R-waves, LFP, and very-low-frequency spectral power. Based on these, additional analytical calculations could be made to provide early warnings of impending cardiac conditions.

Current literature suggests that theory of mind (ToM) and recognition of facial emotion are impaired in behavioral variant frontotemporal dementia (bvFTD). In contrast, studies suggest that ToM is spared in Alzheimer disease (AD). However, there is controversy whether recognition of emotion in faces is impaired in AD. This study challenges the concepts that ToM is preserved in AD and that recognition of facial emotion is impaired in bvFTD. ToM, recognition of facial emotion, and identification of emotions associated with video vignettes were studied in bvFTD, AD, and normal controls. ToM was assessed using false-belief and visual perspective-taking tasks. Identification of facial emotion was tested using Ekman and Friesen's pictures of facial affect. After adjusting for relevant covariates, there were significant ToM deficits in bvFTD and AD compared with controls, whereas neither group was impaired in the identification of emotions associated with video vignettes. There was borderline impairment in recognizing angry faces in bvFTD. Patients with AD showed significant deficits on false belief and visual perspective taking, and bvFTD patients were impaired on second-order false belief. We report novel findings challenging the concepts that ToM is spared in AD and that recognition of facial emotion is impaired in bvFTD.

What the Journal of Electrocardiology can do for electrocardiogram technologists: An electrocardiogram Technologist's Perspective

—ECG (electrocardiogram) currently is the most effective way to detect the heart condition. Previously people have to do the ECG recording in hospital. But the ECG abnormal situation is unsustainable, because the symptoms usually disappear when the patient is sent to the hospital. This may lead to losing the early treatment opportunity. In this paper, a Holter system is designed based on STM32 and FPGA dual-core. ECG data acquisition, LCD display, and data storage in SD card are accomplished. In addition, an ECG de-noised method which combines Stationary Wavelet Transform with Wavelet Transform Scale factor Estimate is accomplished on FPGA to eliminate the noise of ECG. The system can be used anywhere at any time, so it's an ideal device for remote health care. Keywords-ECG; FPGA; STM32; SWT; WTSE; de-noise I. I NTRODUCTION Along with the rapid economic development as well as changes in lifestyle, cardiovascular disease has become one of the most important reasons that cause death recently. People may lose lives even when the heart doesn’t work only for a second. ECG signal objectively expresses the heart activity and is a typical biological electric signal. The correct diagnosis of cardiovascular disease depends on the accuracy of ECG acquisition. ECG recorders are often called Holter recorders, named after the first ECG recorder developed by N. J. Holter in 1961[1]. ECG signal is a kind of very weak electrical signal whose amplitude is only 0.05mV-5mV, and the spectrum scope mainly focus on 0.05 ~ 100Hz[2], so it's often drowned in Strong Noise Jamming. Therefore ECG signal has a disadvantage of low signal-to-noise ratio (SNR). However we can’t extract useful ECG characteristics from the distortion waveform, so restraining and removing the noise from ECG effectively is very important in ECG single processing. The Holter recorder described in this paper is based on STM32 and FPGA dual-core. Taking as main processor, STM32 is used to manage the whole system, such as input scan, wave display, data storage in SD card, and operate the FPGA. Taking as co-processor, FPGA consists a large number of programmable logic blocks, which can each implement an amount of digital logic, and have a high execution speed, so FPGA is used to run the filtering algorithm to meet the requirements of real-time processing, the Analog-to-digital converter module is also operated by FPGA. Two cores are connected by Parallel bus. II. HARDWARE DESIGN In the acquisition system, the hardware consists of electrodes, amplifier circuit, Analog-to-digital converter module, FPGA, STM32, SD Card and LCD module. The system-level description is shown in Figure1.

The article is devoted to the application of machine learning methods for computerized detection of sleep apnea episodes based on the analysis of single-channel signals of the electrocardiogram (ECG) and electroencephalogram (EEG). To study the possibilities of machine learning to detect apnea based on ECG and EEG analysis, we used Apnea-ECG database and MIT-BIH polysomnographic database from PhysioNet, which contain annotations to each minute of records indicating the presence or absence of apnea/hypopnea at the current time. In order to apply machine learning methods to the problem of automated detection of sleep apnea/hypopnea episodes in ECG and EEG signals, long-term polysomnograms available in MIT-BIH polysomnographic database were segmented according to annotations into shorter sections lasting 30 seconds each. The study used 267 segments lasting 30 seconds for the class "norm", 258 segments for the class "apnea" and 273 segments for the class "hypopnea", a total of 798 simultaneous ECG and EEG recordings. The aim of this work is to identify and compare informative signs of sleep apnea episodes in terms of heart rate variability (HRV) and brain electrical activity, as well as the choice of classification methods that provide the highest accuracy for this task. Features of cardiorhythmograms in time and frequency domains, spectral-temporal and wavelet characteristics, as well as parameters of EEG signals based on energy ratio of EEG rhythms, Hearst index, Higuchi fractal dimension and sample entropy for EEG signals are considered. Using different sets of features, the accuracy of classifiers based on decision trees, discriminant analysis, support vector machines, k-nearest neighbor method, and ensemble training was determined. Based on this, combination of features and classifiers is proposed, which provides the highest accuracy of recognition of sleep apnea episodes according to single-channel ECG and EEG signals, taken separately and in the case of a combination of their features. The best results of classification of signals "norm", "apnea" and "hypopnea" were obtained for the model trained using weighted method k nearest neighbors with 25 features of HRV: the total percentage of correctly identified cases for three classes was 99.9% (797 correctly identified cases of 798). By reducing the number of HRV parameters to 9, the best machine learning result was achieved using the bagging ensemble algorithm with 30 decision trees: the total percentage of correctly identified cases for all three classes was 99.4% (793 correctly identified cases from 798: for "norm" - 265 cases from 267, for "apnea" - 257 cases from 258, for "hypopnea" - 271 cases from 273). The use of EEG parameters as features for apnea/hypopnea recognition showed worse results compared to HRV parameters. In this case, the best result of machine learning was achieved using support vector machines with quadratic kernel function: the total percentage of correctly identified cases for three classes was 91.9% and the signals corresponding to norm were most badly recognized (27 cases were classified as hypopnea, and in 9 cases - as sleep apnea). The combination of HRV and EEG parameters gave the best accuracy of 99.1%, but the results are comparable to using only HRV parameters. The obtained results indicate that HRV parameters allow recognizing sleep apnea and hypopnea with higher accuracy than EEG parameters, but EEG signal undoubtedly reflects signs of sleep apnea/hypopnea and also can be used for apnea recognition.

Context-aware computing has become an essential component of modern wearable technology, enabling devices to sense, interpret, and respond to users’ environments and affective states. By integrating contextual intelligence, smart systems can adapt their own behavior to better align with user needs, enhancing efficiency, personalization, and user experience. A critical aspect of context awareness is the ability to recognize a user’s internal state, particularly emotions, which influence decision-making, communication, and overall well-being. While emotion recognition is traditionally approached through multimodal sensing—using cameras, microphones, or physiological sensors such as Electroencephalography (EEG), Electrocardiography (ECG), and Electro-dermal Activity (EDA)—these methods often require multiple sensors, leading to challenges related to power consumption, complexity, and privacy. The demand for a more efficient and unobtrusive approach to emotion and context recognition has driven research toward minimal-sensor solutions that can extract meaningful affective and contextual information with reduced hardware requirements. Restricting sensing sources of contextual information by using only limited number of sensors might come at the expense of constraining the ultimate goal of context-awareness. Such expense can be mitigated by “doing more with less” i.e. delving into the opportunities of using a single sensor for multiple purposes. What is desirable is a minimal-sensor solution capable of capturing both motion and physiological signals for both robust emotion recognition and additional context estimation. Among wearable sensors, chest-worn Inertial Measurement Units (IMUs) stand out as promising candidates for comprehensive context estimation, as they are well-positioned to capture motion patterns, and physiological vibrations, being the subtle cardio-respiratory signals. This raises the question of whether a single chest-worn IMU could serve as a minimal yet effective sensor for simultaneously estimating both emotional and contextual information, offering a practical alternative to traditional multi-sensor approaches. To address the challenge of using chest-worn IMU for multiple context sensing, I investigated the potential of using Seismocardiography (SCG) and Accelerometry-Derived Respiration (ADR) to estimate emotional states. First, I explored the existing capabilities of chest-worn IMUs in capturing cardio-respiratory vibrations and motion data, which I used as a knowledge base for embedding such IMUs in context sensing. Data plays a crucial role in this problem, therefore I developed a flexible software interface, ColEmo, to facilitate the scalable collection of emotion-related data and introduced a novel dataset, EmoWear, integrating physiological and motion data for emotion recognition and context awareness. Then, I examined the feasibility of using SCG for emotion recognition, comparing its performance with traditional physiological signals and evaluating its effectiveness as a single-sensor solution. This thesis presents a detailed analysis of the potential of chest-worn IMUs for emotion and context recognition. It reports on the design and validation of a SCG-based wearable system, the development and characteristics of the EmoWear dataset, and the implementation of the ColEmo platform for emotion data collection. The research also examines machine learning techniques applied to the SCG and ADR signals, assessing their effectiveness in estimating emotional states. My findings indicate that SCG-based emotion recognition achieves performance comparable to conventional physiological counterparts, demonstrating the feasibility of using a single IMU sensor for affective computing. The publicly-available EmoWear dataset provides a valuable resource for future research, highlighting the potential of combining motion and physiological data for enhanced context awareness. Additionally, the ColEmo software interface, which I published open-source, proves to be an effective tool for multi-modal emotion data collection, facilitating further advancements in wearable emotion recognition. The results pave the way for a minimal-sensor approach for context information estimation that can significantly reduce system complexity while maintaining high performance. These findings confirm that chest-worn IMUs present an opportunity to further improve emotion recognition in addition to the motion-based context information estimation. My research highlights the need for standardized methodologies in affective computing and demonstrates the viability of motion-based physiological sensing for emotion estimation. By using a single chest-worn IMU, this work presents an alternative to multi-sensor systems, reducing power consumption, and privacy concerns while maintaining robust performance. Future research should focus on refining IMU-based emotion recognition using deep learning techniques and larger datasets to enhance classification accuracy. Further validation in real-world settings is necessary to assess the practical applicability of the proposed system. The EmoWear dataset should be explored for additional context-awareness applications, such as activity recognition and stress monitoring, to maximize its utility. The ColEmo platform offers opportunities for expansion beyond emotion recognition, including cognitive load estimation, stress monitoring, and human-computer interaction. This research moves wearable technology forward by using fewer sensors, helping make affective computing more efficient, versatile, and respectful of privacy.

Surface Electrocardiogram Recording: Baseline 12-lead and Ambulatory Electrocardiogram Monitoring.

The deceleration capacity – a new measure of heart rate variability evaluated in patients with schizophrenia and antipsychotic treatment

A precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed.