Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model

Exploiting Wavelet and Prosody-related Features for the Detection of Voice Disorders

An approach for the detection of voice disorders exploiting wavelet and prosody-related properties of speech is presented in this paper. Based on the normalized energy contents of the Discrete Wavelet Transform (DWT) coefficients over all voice frames, several statistical measures are first determined. Then, the idea of some prosody-related voice properties, such as mean pitch, jitter and shimmer are utilized to compute similar statistical measures over all the frames. A set of statistical measures of the normalized energy contents of the DWT coefficients is combined with a set of statistical measures of the extracted prosody-related voice properties in order to form a feature vector to be used in both training and testing phases. Two categories of voice samples namely, healthy and disordered are considered here thus formulating the problem in the proposed method as a two-class problem to be solved. Finally, an Euclidean Distance based classifier is used to handle the feature vector for the purpose of detecting the disordered voice. A number of simulations is carried out and it is shown that the statistical analysis based on wavelet and prosody-related properties can effectively detect a variety of voice disorders from the mixture of healthy and disordered voices

New time-frequency domain pitch estimation methods for speed signals under low levels of SNR

The major objective of this research is to develop novel pitch estimation methods capable of handling speech signals in practical situations where only noise-corrupted speech observations are available. With this objective in mind, the estimation task is carried out in two different approaches. In the first approach, the noisy speech observations are directly employed to develop two new time-frequency domain pitch estimation methods. These methods are based on extracting a pitch-harmonic and finding the corresponding harmonic number required for pitch estimation. Considering that voiced speech is the output of a vocal tract system driven by a sequence of pulses separated by the pitch period, in the second approach, instead of using the noisy speech directly for pitch estimation, an excitation-like signal (ELS) is first generated from the noisy speech or its noise- reduced version. In the first approach, at first, a harmonic cosine autocorrelation (HCAC) model of clean speech in terms of its pitch-harmonics is introduced. In order to extract a pitch-harmonic, we propose an optimization technique based on least-squares fitting of the autocorrelation function (ACF) of the noisy speech to the HCAC model. By exploiting the extracted pitch-harmonic along with the fast Fourier transform (FFT) based power spectrum of noisy speech, we then deduce a harmonic measure and a harmonic-to-noise-power ratio (HNPR) to determine the desired harmonic number of the extracted pitch-harmonic. In the proposed optimization, an initial estimate of the pitch-harmonic is obtained from the maximum peak of the smoothed FFT power spectrum. In addition to the HCAC model, where the cross-product terms of different harmonics are neglected, we derive a compact yet accurate harmonic sinusoidal autocorrelation (HSAC) model for clean speech signal. The new HSAC model is then used in the least-squares model-fitting optimization technique to extract a pitch-harmonic. In the second approach, first, we develop a pitch estimation method by using an excitation-like signal (ELS) generated from the noisy speech. To this end, a technique is based on the principle of homomorphic deconvolution is proposed for extracting the vocal-tract system (VTS) parameters from the noisy speech, which are utilized to perform an inverse-filtering of the noisy speech to produce a residual signal (RS). In order to reduce the effect of noise on the RS, a noise-compensation scheme is introduced in the autocorrelation domain. The noise-compensated ACF of the RS is then employed to generate a squared Hilbert envelope (SHE) as the ELS of the voiced speech. With a view to further overcome the adverse effect of noise on the ELS, a new symmetric normalized magnitude difference function of the ELS is proposed for eventual pitch estimation. Cepstrum has been widely used in speech signal processing but has limited capability of handling noise. One potential solution could be the introduction of a noise reduction block prior to pitch estimation based on the conventional cepstrum, a framework already available in many practical applications, such as mobile communication and hearing aids. Motivated by the advantages of the existing framework and considering the superiority of our ELS to the speech itself in providing clues for pitch information, we develop a cepstrum-based pitch estimation method by using the ELS obtained from the noise-reduced speech. For this purpose, we propose a noise subtraction scheme in frequency domain, which takes into account the possible cross-correlation between speech and noise and has advantages of noise being updated with time and adjusted at each frame. The enhanced speech thus obtained is utilized to extract the vocal-tract system (VTS) parameters via the homomorphic deconvolution technique. A residual signal (RS) is then produced by inverse-filtering the enhanced speech with the extracted VTS parameters. It is found that, unlike the previous ELS-based method, the squared Hilbert envelope (SHE) computed from the RS of the enhanced speech without noise compensation, is sufficient to represent an ELS. Finally, in order to tackle the undesirable effect of noise of the ELS at a very low SNR and overcome the limitation of the conventional cepstrum in handling different types of noises, a time-frequency domain pseudo cepstrum of the ELS of the enhanced speech, incorporating information of both magnitude and phase spectra of the ELS, is proposed for pitch estimation. (Abstract shortened by UMI.

EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN

Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable challenges in the realm of neuromuscular disorder diagnostics. In this study, we employ advanced deep-learning techniques to address the detection of ALS and Myopathy, two debilitating conditions. Our methodology begins with the extraction of informative features from raw electromyography (EMG) signals, leveraging the Log-spectrum, and Delta Log spectrum, which capture the frequency contents, and spectral and temporal characteristics of the signals. Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with Convolutional Neural Networks (CNNs) and Attention for the classification of three classes. The robustness of our approach is rigorously evaluated, demonstrating its remarkable performance in distinguishing among the classes: Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection

ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals

Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detect ALS and Myopathy. Convolutional Neural Networks (CNNs) have emerged as powerful tools in this context. We present ResEMGNet, designed to identify ALS and Myopathy directly from raw electromyography (EMG) signals. Unlike traditional methods that require intricate handcrafted feature extraction, ResEMGNet takes raw EMG data as input, reducing computational complexity and enhancing practicality. Our approach was rigorously evaluated using various metrics in comparison to existing methods. ResEMGNet exhibited exceptional subject-independent performance, achieving an impressive overall three-class accuracy of 94.43\%