Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review.

Obstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images.

One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.

Advanced Time-Frequency Methods for ECG Waves Recognition.

ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required to extract representative features. This paper studies the performance of two different spectrum representations, iris-spectrogram and scalogram, for different ECG beat waves in terms of recognition of normal, tachycardia, and bradycardia classes. These two different spectra are then sent to two different deep convolutional neural networks (CNN), i.e., Resnet101 and ShuffleNet, for deep feature extraction and classification. The results show that the best accuracy for detection of beats rhythm was using ResNet101 and scalogram of T-wave with an accuracy of 98.3%, while accuracy was 94.4% for detection using iris-spectrogram using also ResNet101 and QRS-Wave. Finally, based on these results we note that using deep features from time-frequency representation using one wave of ECG beat we can accurately detect basic rhythms such as normal, tachycardia, and bradycardia.

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds.

In recent years deep learning models improve the diagnosis performance of many diseases especially respiratory diseases. This paper will propose an evaluation for the performance of different deep learning models associated with the raw lung auscultation sounds in detecting respiratory pathologies to help in providing diagnostic of respiratory pathologies in digital recorded respiratory sounds. Also, we will find out the best deep learning model for this task. In this paper, three different deep learning models have been evaluated on non-augmented and augmented datasets, where two different datasets have been utilized to generate four different sub-datasets. The results show that all the proposed deep learning methods were successful and achieved high performance in classifying the raw lung sounds, the methods were applied on different datasets and used either augmentation or non-augmentation. Among all proposed deep learning models, the CNN-LSTM model was the best model in all datasets for both augmentation and non-augmentation cases. The accuracy of CNN-LSTM model using non-augmentation was 99.6%, 99.8%, 82.4%, and 99.4% for datasets 1, 2, 3, and 4, respectively, and using augmentation was 100%, 99.8%, 98.0%, and 99.5% for datasets 1, 2, 3, and 4, respectively. While the augmentation process successfully helps the deep learning models in enhancing their performance on the testing datasets with a notable value. Moreover, the hybrid model that combines both CNN and LSTM techniques performed better than models that are based only on one of these techniques, this mainly refers to the use of CNN for automatic deep features extraction from lung sound while LSTM is used for classification.

A lightweight hybrid deep learning system for cardiac valvular disease classification.

Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical big data and Artificial Intelligence (AI) technology encouraged the effort to develop and deploy deep learning models for distinguishing heart sound abnormalities. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. In this research, a cardiac diagnostic system that combined CNN and LSTM components was developed, it uses phonocardiogram (PCG) signals, and utilizes either augmented or non-augmented datasets. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. For the five classes problem using the open heart sound dataset, accuracy was 98.5%, F1-score was 98.501%, and Area Under the Curve (AUC) was 0.9978 for the non-augmented dataset and accuracy was 99.87%, F1-score was 99.87%, and AUC was 0.9985 for the augmented dataset. Model performance was further evaluated using the PhysioNet/Computing in Cardiology 2016 challenge dataset, for the two classes problem, accuracy was 93.76%, F1-score was 85.59%, and AUC was 0.9505. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.

Sliding window based deep ensemble system for breast cancer classification.

Breast cancer is a severe problem for women around the world especially in developing countries, according to recent reports from the World Health Organization (WHO). High accuracy and early detection of breast cancer reduces the mortality rate, in the other hand, recognition of breast cancer is a complicated issue. Various studies and methods have been carried out to overcome this problem and to obtain accurate screening of breast cancer. One of the most recent methods with high performance is deep learning; it has been used to classify breast cancer using mammograms or histopathological images. This paper proposes a new using the concept of sliding window, and using the ensemble of four pre-trained convolutional neural networks (CNN) in order to classify breast cancer into eight classes. In this study, each image produces 4 non-overlapped sliding windows which are fed to GoogleNet, AlexNet, ResNet50, and DenseNet-201 CNNs, and an ensemble is then done to find the major class of each window, the ensemble is then applied again to find the class of the whole histopathological image. Breast Cancer Histopathological Database (BreakHis) database has been employed in this paper with eight classes (Adenosis, Ductal Carcinoma, Fibroadenoma, Lobular Carcinoma, Mucinous Carcinoma Papillary Carcinoma, Phyllodes Tumour, Tubular Adenoma). The proposed method is applied to four magnification cases: 40x, 100x, 200x, and 400x images. The proposed ensemble technique achieved an accuracy of 99.3325%. The results of the proposed system are comparable to recent studies results.

Multiple time and spectral analysis techniques for comparing the PhotoPlethysmography to PiezoelectricPlethysmography with electrocardiography.

Photoplethysmography (PPG) is an important, non-invasive and widely used circulatory assessment technique. It is commonly used to measure heart rate and arterial oxygen saturation (SPO2) by measuring the changes occurred in the blood volume and shows many future perspective applications. In this paper, various time and frequency analysis techniques are used to investigate the spectral differences of the signals obtained using the PPG and the piezoelectricplethysmography (PEPG) techniques. The time delay, effect of respiration and motion artifacts have been investigated in time and frequency domain for both; the PPG and PEPG signals. The electrocardiograph (ECG) signal has been used as a reference. The heart-rate has been estimated using both signals; the PPG and PEPG. The hypothesis of this paper is that PPG and PEPG signals features integration can lead to improve the understanding and estimation of the human body's vital signs by including multi-dimensional features. The results show that the PPG signal is the most robust technique in terms of change in frequency and time domains under the same conditions. Additionally, the PPG signal is less sensitive to artifacts compared to the PEPG signal. Such a study opens possibilities to consider the PPG signal for a wide range of biomedical applications especially in wearable biomedical technologies to utilize its non-invasive property.

AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the leading causes of blindness being diagnosed using OCT. Recently, by developing machine learning and deep learning techniques, the classification of eye retina diseases using OCT images has become quite a challenge. In this paper, a novel automated convolutional neural network (CNN) architecture for a multiclass classification system based on spectral-domain optical coherence tomography (SD-OCT) has been proposed. The system used to classify five types of retinal diseases (age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen) in addition to normal cases. The proposed CNN architecture with a softmax classifier overall correctly identified 100% of cases with AMD, 98.86% of cases with CNV, 99.17% cases with DME, 98.97% cases with drusen, and 99.15% cases of normal with an overall accuracy of 95.30%. This architecture is a potentially impactful tool for the diagnosis of retinal diseases using SD-OCT images.

Towards classifying non-segmented heart sound records using instantaneous frequency based features.

Heart sound and its recorded signal which is known as phonocardiograph (PCG) are one of the most important biosignals that can be used to diagnose cardiac diseases alongside electrocardiogram (ECG). Over the past few years, the use of PCG signals has become more widespread and researchers pay their attention to it and aim to provide an automated heart sound analysis and classification system that supports medical professionals in their decision. In this paper, a new method for heart sound features extraction for the classification of non-segmented signals using instantaneous frequency was proposed. The method has two major phases: the first phase is to estimate the instantaneous frequency of the recorded signal; the second phase is to extract a set of eleven features from the estimated instantaneous frequency. The method was tested into two different datasets, one for binary classification (Normal and Abnormal) and the other for multi-classification (Five Classes) to ensure the robustness of the extracted features. The overall accuracy, sensitivity, specificity, and precision for binary classification and multi-classification were all above 95% using both random forest and KNN classifiers.

Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features.

Electrocardiogram (ECG) beat classification is a significant application in computer-aided analysis and diagnosis technologies. This paper proposed a method to detect, extract informative features, and classify ECG beats utilizing real ECG signals available in the standard MIT-BIH Arrhythmia database, with 10,502 beats had been extracted from it. The present study classifies the ECG beat into six classes, normal beat (N), Left bundle branch block beat, Right bundle branch block beat, Premature ventricular contraction, atrial premature beat, and aberrated atrial premature, using Gaussian mixture and wavelets features, and by applying principal component analysis for feature set reduction. The classification process is implemented utilizing two classifier techniques, the probabilistic neural network (PNN) algorithm and Random Forest (RF) algorithm. The achieved accuracy is 99.99%, and 99.97% for PNN and RF respectively. The precision is 99.99%, and 99.98% for PNN and RF respectively. The sensitivity is 99.99%, and 99.81% for PNN and RF respectively, while the specificity is 99.97%, 99.96% for PNN and RF respectively. It has been shown that the combination of Gaussian mixtures coefficients and the wavelets features have provided a valuable information about the heart performance and can be used significantly in arrhythmia classification.

An enhanced method for real-time modelling of cardiac related biosignals using Gaussian mixtures.

Cardiac related biosignals modelling is very important for detecting, classification, compression and transmission of such health-related signals. This paper introduces a new, fast and accurate method for modelling the cardiac related biosignals (ECG and PPG) based on a mixture of Gaussian waves. For any signal, at first, the start and end of the ECG beat or PPG pulse is detected, then the baseline is detected then subtracted from the original signal, after that the signal is divided into two signals positive and negative, each modelled separately then incorporated together to form the modelled signal. The proposed method is applied in the MIMIC, and MIT-BIH Arrhythmia databases available online at PhysioNet.