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Cardiovascular is nowadays a common and threatening disease for humans. Computer-aided diagnostic (CAD) can diagnose cardiovascular by finding anomalies in an electrocardiogram (ECG). However, this conventional diagnostic approach is inefficient and needs extensive analysis and medical knowledge to diagnose accurately. Deep learning can help in the...
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Citations
... Innovations like the 2D-wavelet encoded deep CNN for image-based ECG classification by Mewada highlight the integration of multi-spectral information with temporal features to enhance classification accuracy, marking a shift towards more nuanced diagnostic methodologies [11]. Similarly, Koresh's examination of preprocessing steps in deep learning-based image classifications elucidates the balance between image enhancement and the preservation of essential information, providing insights into data preparation for deep learning models [12]. ...
Identifying and accurately classifying cardiac abnormalities, including myocardial infarction (MI) and cardiac arrhythmia (CA), remains a significant challenge in the field of cardiology, largely due to the limitations inherent in traditional ECG signal analysis techniques. This paper presents an innovative method aimed at addressing this challenge. By implementing a novel transformation technique, we map temporal, frequency-based, statistical, and spatial features of ECG signals onto the R, G, and B channels of an RGB image. This conversion process results in a feature-rich representation of the ECG signal, significantly enhancing its clinical relevance and thus maximizing classification accuracy. Utilizing an adaptive RGB-ResNet inception architecture, our approach achieves remarkable average accuracies of 99.25% for myocardial infarction and 99.21% for cardiac arrhythmia. These figures underscore the robustness of our method and highlight its significant potential to advance cardiology diagnostics through the application of advanced image analysis techniques.
... In recent years however, there has been a notable increase in interest surrounding the novel utilization of deep learning (DL) models, such as convolutional and recurrent neural networks, for enhanced CAD detection directly from raw or minimally processed ECG signal data [13][14][15][16][17]. In contrast to conventional ML approaches, end-to-end DL models present a significant advancement in automating feature engineering and capturing intricate discriminative patterns within ECG morphological data. ...
... Mel frequency cepstral coefficients (MFCCs), linear prediction coefficients (LPCs), linear prediction cepstral coefficients (LPCCs), line spectral frequencies (LSFs), discrete wavelet transform (DWT) [3,4], and perceptual linear prediction (PLP) are speech feature extractions commonly used in speaker recognition as well as speaker spoofing identification [5]. A wavelet transform was used to obtain spectral features, and these features were integrated with CNN's spatial features in Reference [6] for ECG classification. In Reference [7], the authors analyzed a 6-8 kHz high-frequency subband using CQCC features to investigate re-recording distortion. ...
Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred to as "speech spoofing". The algorithms used in spoof attacks are practically unknown; hence, further analysis and development of spoof-detection models for improving spoof classification are required. A study of the spoofed-speech spectrum suggests that high-frequency features are able to discriminate genuine speech from spoofed speech well. Typically, linear or triangular filter banks are used to obtain high-frequency features. However, a Gaussian filter can extract more global information than a triangular filter. In addition, MFCC features are preferable among other speech features because of their lower covariance. Therefore, in this study, the use of a Gaussian filter is proposed for the extraction of inverted MFCC (iMFCC) features, providing high-frequency features. Complementary features are integrated with iMFCC to strengthen the features that aid in the discrimination of spoof speech. Deep learning has been proven to be efficient in classification applications, but the selection of its hyper-parameters and architecture is crucial and directly affects performance. Therefore, a Bayesian algorithm is used to optimize the BiLSTM network. Thus, in this study, we build a high-frequency-based optimized BiLSTM network to classify the spoofed-speech signal, and we present an extensive investigation using the ASVSpoof 2017 dataset. The optimized BiLSTM model is successfully trained with the least epoch and achieved a 99.58% validation accuracy. The proposed algorithm achieved a 6.58% EER on the evaluation dataset, with a relative improvement of 78% on a baseline spoof-identification system.
... Diseases of the heart are often referred to as CVDs. According to a study prepared for the World Heart Federation (WHF) [10] in 2020 by the Pan-African Society of Cardiology [11], cardiovascular disease is accountable for 11.85% of all deaths in Cameroon. Common signs of a heart attack include nausea, vomiting, and chilly sweats [12]. ...
... Despite all these difficulties, methods for ECG arrhythmia classification have been widely explored [11,12] but choosing the best technique for smart patient monitoring depends on the robustness and performance of these methods. Several convolutional neural network (CNN)-based approaches have been introduced for the task [13,14]. ...
Early diagnosis and classification of arrhythmia from an electrocardiogram (ECG) plays a significant role in smart healthcare systems for the health monitoring of individuals with cardiovascular diseases. Unfortunately, the nonlinearity and low amplitude of ECG recordings make the classification process difficult. Thus, the performance of most traditional machine learning (ML) classifiers is questionable, as the interrelationship between the learning parameters is not well modeled, especially for data features with high dimensions. To address the limitations of ML classifiers, this paper introduces an automatic arrhythmia classification approach based on the integration of a recent metaheuristic optimization (MHO) algorithm and ML classifiers. The role of the MHO is to optimize the search parameters of the classifiers. The approach consists of three steps: the preprocessing of the ECG signal, the extraction of the features, and the classification. The learning parameters of four supervised ML classifiers were utilized for the classification task; support vector machine (SVM), k-nearest neighbors (kNNs), gradient boosting decision tree (GBDT), and random forest (RF) were optimized using the MHO algorithm. To validate the advantage of the proposed approach, several experiments were conducted on three common databases, including the Massachusetts Institute of Technology (MIT-BIH), the European Society of Cardiology ST-T (EDB), and the St. Petersburg Institute of Cardiological Techniques 12-lead Arrhythmia (INCART). The obtained results showed that the performance of all the tested classifiers were significantly improved after integrating the MHO algorithm, with the average ECG arrhythmia classification accuracy reaching 99.92% and a sensitivity of 99.81%, outperforming the state-of the-art methods.
Deep learning softwares are designed using artificial neural networks for various applications by training and testing them with an appropriate dataset. The raw image samples available in the dataset may contain noisy and unclear information due to radiation, heat and poor lighting conditions. Therefore, the researchers are trying to filter and enhance such noisy images through preprocessing steps for providing a valid feature information to the neural network layers included in the deep learning software. However, there are certain claims that roam around the researchers such as an image may lose some useful information when it is not preprocessed with an appropriate filter or enhancement technique. Hence, the work reviews the efficacy of the methodologies that are designed with and without a preprocessing step. Also, the work summarizes the common reasons and statements highlighted by the researchers for using and avoiding the preprocessing steps on designing a deep learning approach. The study is conducted to provide a clarity toward the requirement and non-requirement of preprocessing step in a deep learning software.
