New Features for the Detection of Fetal QRS Complexes in Non-Invasive Fetal Electrocardiograms.

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

ECGPsychNet: an optimized hybrid ensemble model for automatic detection of psychiatric disorders using ECG signals.

\textbf{Background:} Psychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BD), and depression (DPR) are one of the leading causes of disability and suicide worldwide. The signs and symptoms of SCZ, BD, and DPR vary dynamically and do not have uniform detection strategies. The main causes of delays in the detection of psychiatric disorders are negligence by immediate caregivers, varying symptoms, stigma and limited availability of physiological signals. \textbf{Motivation:} The brain functionality in the patients with SCZ, BD, and DPR changes compared to the normal cognition population. The brain-heart interaction plays a crucial role to track the changes in cardiac activities during such disorders. Therefore, this paper explores the application of electrocardiogram (ECG) signals for the detection of three psychiatric (SCZ, BD, and DPR) disorders. \textbf{Method:} This paper develops ECGPsychNet an ensemble decomposition and classification technique for the automated detection of SCZ, BD, and DPR using ECG signals. Three well-known decomposition techniques empirical mode decomposition, variational mode decomposition, and tunable Q wavelet transform (TQWT) are used to decompose the ECG signals in to various subbands(SBs). Various features are extracted from the different SBs and classified using optimizable ensemble techniques using two validation techniques. \textbf{Results:} The developed ECGPsychNet has obtained the highest classification accuracy of 98.15\% using the features from the sixth SB of TQWT. Our proposed model has the highest detection rate of 98.96\%, 96.04\%, and 95.12\% for SCZ, DPR, and BD. \textbf{Conclusions:} Our developed prototype is able to detect SCZ, DPR and BD using ECG signals. However, the automated ECGPsychNet is ready to be tested with more dataset belonging to different races and age groups.

CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals.

The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of sleep instability. The CAP can examine various sleep-related disorders. Certain short events (A and B phases) manifest related to a specific physiological process or pathology during non-rapid eye movement (NREM) sleep. These phases unexpectedly modify EEG oscillations; hence, manual detection is challenging. Therefore, it is highly desirable to have an automated system for detecting the A-phases (AP). Deep convolution neural networks (CNN) have shown high performance in various healthcare applications. A variant of the deep neural network called the Wavelet Scattering Network (WSN) has been used to overcome the specific limitations of CNN, such as the need for a large amount of data to train the model. WSN is an optimized network that can learn features that help discriminate patterns hidden inside signals. Also, WSNs are invariant to local perturbations, making the network significantly more reliable and effective. It can also help improve performance on tasks where data is minimal. In this study, we proposed a novel WSN-based CAPSCNet to automatically detect AP using EEG signals. Seven dataset variants of cyclic alternating pattern (CAP) sleep cohort is employed for this study. Two electroencephalograms (EEG) derivations, namely: C4-A1 and F4-C4, are used to develop the CAPSCNet. The model is examined using healthy subjects and patients tormented by six different sleep disorders, namely: sleep-disordered breathing (SDB), insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, periodic leg movement disorder (PLM) and rapid eye movement behavior disorder (RBD) subjects. Several different machine-learning algorithms were used to classify the features obtained from the WSN. The proposed CAPSCNet has achieved the highest average classification accuracy of 83.4% using a trilayered neural network classifier for the healthy data variant. The proposed CAPSCNet is efficient and computationally faster.