Smart helmet: wearable multichannel ECG & EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet

Data Fusion for QRS Complex Detection in Multi-Lead Electrocardiogram Recordings

Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps

An Algorithm Based on the Continuous Wavelet Transform with Splines for the Automatic Measurement of QT Dispersion: Validation and Application in Chronic Kidney Disease

Chronic kidney disease (CKD) is considered a risk factor for the development of cardiovascular disease. QT interval is an electrocardiographic parameter that quantifies the duration of ventricular repolarization. An increase of its spatial variability measured from the selected leads of a standard electrocardiogram (ECG), named QT dispersion (QTd), is considered a risk factor for malign ventricular arrhythmias and sudden death in the CKD. An algorithm for automatic measurement of QTd in the ECG leads DI, aVF and V2 using the continuous wavelet transform with splines is presented. Validation of QRS complex detection has been done on records from MIT-BIH database, and the accuracy is 99.5%. Validation of detection of QRS wave onset and T wave end has been done on records from CSE and QT databases, and the measurements were within the tolerance limits for deviations with respect to the manual measurements defined by the experts. The algorithm was applied in two studies. In the first study, QTd was evaluated in normal subjects and patients with CKD. In the second study, QTd was analyzed in patients with CKD before, during and after the hemodialysis treatment. In both studies, the algorithm had a good performance for the QTd analysis

An Arrhythmia Classification-Guided Segmentation Model for Electrocardiogram Delineation

Accurate delineation of key waveforms in an ECG is a critical initial step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learning based methods using a segmentation model to locate P, QRS and T waves have shown promising results, their ability to handle signals exhibiting arrhythmia remains unclear. In this study, we propose a novel approach that leverages a deep learning model to accurately delineate signals with a wide range of arrhythmia. Our approach involves training a segmentation model using a hybrid loss function that combines segmentation with the task of arrhythmia classification. In addition, we use a diverse training set containing various arrhythmia types, enabling our model to handle a wide range of challenging cases. Experimental results show that our model accurately delineates signals with a broad range of abnormal rhythm types, and the combined training with classification guidance can effectively reduce false positive P wave predictions, particularly during atrial fibrillation and atrial flutter. Furthermore, our proposed method shows competitive performance with previous delineation algorithms on the Lobachevsky University Database (LUDB)

MS

thesisAccurate QRS detection is essential in online computerized rhythm monitoring systems. A major cause of error in QRS detection schemes arises from artifacts superimposed on the input signal. To a lesser extent identification of P or T waves as QRS complexes can represent another source of error. In an effort to reduce the incidence of false and missed alarms generated by the rhythm monitoring system currently used in the LDS Hospital Coronary Care Unit, a project was undertaken to improve the accuracy and reliability of the QRS detection algorithm, specifically in contaminated single lead electrocardiographic data. The algorithm uses a dual scan of the sample data combined with a peak detection scheme to locate a reference point on a QRS candidate. The candidate is then checked for evidence of baseline shift or an excessively low signal-to-noise ratio. If neither of these criteria is met, the candidate is assumed to be QRS and a fiducial point is located on the complex. To assess the sensitivity and specificity of the QRS detection algorithm, an off-line evaluation was performed on forty-one patient records collected in the Coronary Care Unit. Arrhythmias included in the evaluation were fast ventricular and atrial rhythms and heart block. Over 90 percent of the data base was contaminated with excessive muscle artifacts. Of a total of 7,205 beats used in the evaluation, and positive predictive accuracy were .9641 and .9573, respectively. Of the error, 92.16 percent of the false positives and 84.17 percent of the false negatives were due to excessive noise spike superimposition on the data. None of the false positive error (.0071) was due to P or T wave misidentification of a QRS complex. These results indicate that a signal-in-noise approach to automated QRS detection is effective in identifying QRS complexes in the contaminated single lead electrocardiogram with minimal error

Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation

Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards

Embedded system for individual recognition based on ECG biometrics

Biometric recognition is emerging has an alternative solution for applications where the privacy of the information is crucial. This paper presents an embedded biometric recognition system based on the Electrocardiographic signals (ECG) for individual identification and authentication. The proposed system implements a real-time state-of-the-art recognition algorithm, which extracts information from the frequency domain. The system is based on a ARM Cortex 4. Preliminary results show that embedded platforms are a promising path for the implementation of ECG-based applications in real-world scenario