Sleep apnea is a potentially life-threatening sleep condition in which breathing stops and resumes repeatedly. It is caused by breathing pauses during sleep, which leads to frequent awakenings. As we all know, computational time and efficiency are essential in the healthcare industry; to address this issue, we proposed an algorithm that performs more computations in less time without compromising the machine learning model’s performance. This study employs a unique technique called Sliding Singular Spectrum Analysis (SSSA) to decompose and de-noise the ECG signals. To identify the significant apnea and non-apnea components from the pre-processed ECG data and to reduce the dimensionality, we used Principal Component Analysis (PCA), Kernal PCA (KPCA), and Sub-Pattern based PCA (SPPCA). These characteristics were then used to train and evaluate various machine learning models, including KNN, SVM, GaussianNB, SGD, and XGBoost, to distinguish between apnea and non-apnea ECG data. The publicly available Physionet Apnea-ECG database is used for the simulation of the proposed algorithm. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

Traditionally, the heart sound classification process is performed by first finding the elementary heart sounds of the phonocardiogram (PCG) signal. After detecting sounds S1 and S2, the features like envelograms, Mel frequency cepstral coefficients (MFCC), kurtosis, etc., of these sounds are extracted. These features are used for the classification of normal and abnormal heart sounds, which leads to an increase in computational complexity. In this paper, we have proposed a fully automated algorithm to localize heart sounds using K-means clustering. The K-means clustering model can differentiate between the primitive heart sounds like S1, S2, S3, S4 and the rest of the insignificant sounds like murmurs without requiring the excessive pre-processing of data. The peaks detected from the noisy data are validated by implementing five classification models with 30 fold cross-validation. These models have been implemented on a publicly available PhysioNet/Cinc challenge 2016 database. Lastly, to classify between normal and abnormal heart sounds, the localized labelled peaks from all the datasets were fed as an input to the various classifiers such as support vector machine (SVM), K-nearest neighbours (KNN), logistic regression, stochastic gradient descent (SGD) and multi-layer perceptron (MLP). To validate the superiority of the proposed work, we have compared our reported metrics with the latest state-of-the-art works. Simulation results show that the highest classification accuracy of 94.75% is achieved by the SVM classifier among all other classifiers.

The automatic and accurate detection of lung tumours or nodules plays a vital role in diagnosing lung cancer. Radiologist have to study multiple chest x-rays to diagnose lung cancer. Since it is a very tedious and time-consuming task, lots of automatic nodule detection and classification techniques based on deep learning have been proposed in recent years. As the previous methods completely rely on deep networks, it increases the computational complexity of the nodule detection process, so in this work, we have proposed a modified wiener filter to remove the adaptive noise from the Chest X-Ray (CXR) images which has not been attempted by any researcher, further to make nodules clearer and contrasting we have enhanced the filtered CXR image by using adaptive histogram equalization (AHE). Later, we have performed classification using the simplified VGG network for Deep Convolutional Neural Network (D-CNN) model to detect and classify the lung nodules from CXR images. As a result, we got an accuracy of 92% in training and 94% on validation, which is then compared with the latest state-of-art works to prove the novelty of our proposed method. The dataset used in our study is taken from the Japan Society of Radiological Technology (JSRT). Finally, we have introduced a Computer Interface (CI) through which the proposed work can be implemented in real-time nodule detection applications in the near future.

Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and non-alcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

Electrocardiogram (ECG) is the graphical portrayal of heart usefulness. The ECG signals holds its significance in the discovery of heart irregularities. These ECG signals are frequently tainted by antiques from various sources. It is basic to diminish these curios and improve the exactness just as dependability to show signs of improvement results identified with heart usefulness. The most commonly disturbed artifact in ECG signals is Motion Artifacts (MA). In this paper, we have proposed a new concept on how machine learning algorithms can be used for de-noising the ECG signals. Towards the goal, a unique combination of Recurrent Neural Network (RNN) and Deep Neural Network (DNN) is used to efficiently remove MA. The proposed algorithm is validated using ECG records obtained from the MIT-BIH Arrhythmia Database. To eliminate MA using the proposed method, we have used Adam optimization algorithm to train and fit the contaminated ECG data in RNN and DNN models. Performance evaluation results in terms of SNR and RRMSE show that the proposed algorithm outperforms other existing MA removal methods without significantly distorting the morphologies of ECG signals.

Diabetic Retinopathy (DR) is an intricacy of dia- betes that affects the eyes. In this paper, we have proposed a hybrid pre-processing and feature extraction technique named as Microaneurysm Retinal vein Haemorrhage Exudate (MRHE) extraction using Feature Enhancement and Edge Detection (FEED) which can extract all the features in a single step and with very less complexity. To classify the presence of DR, we have used an efficient Deep Convolutional Neural Network (D-CNN), model. The D-CNN model is trained with four salient features namely retinal veins, MA’s, exudates, and haemorrhages which were extracted from the raw images using image-processing techniques. After training and testing the D-CNN model, we were able to classify the presence of DR based on the features extracted from the testing data. To implement this proposed method, we have used a dataset from the STructured Analysis of the Retina (STARE) Database, which comprises of retinal images taken under various imaging conditions using fundus photography. To demonstrate the legitimacy of the proposed method, we have compared our method with the existing DR detection and classification methods such as SVM, ANN,etc.. Performance evaluation results in terms of Accuracy and Recall show that the proposed algorithm outperforms other existing DR classification methods.

In this paper, more emphasis has been given to develop various machine learning models using SPPCA and SUBXPCA dimensionality reduction algorithms to increase the classification accuracy. Firstly, Discrete Wavelet Transform (DWT) is applied to EEG signals for extracting the time-frequency domain features of epilepsy such as the energy of each sub-pattern, spike rhythmicity, relative spike amplitude, Dominant Frequency (DF) and Spectral Entropy (SE). The features obtained after performing DWT on an EEG signal are extensive in number, to select the prominent features and to retain their properties, correlation feature sub-pattern-based PCA (SPPCA), and cross sub-pattern correlation-based PCA (SUBXPCA) are used as a dimensionality reduction techniques. To validate the proposed work, performance evaluation parameter such as the accuracy of the time-frequency domain features from different combinations of the dataset has been compared with the latest state-of-the-art works. Simulation results show that the proposed algorithm combined with machine learning classifiers. The best accuracy of 97% for SPPCA is achieved by CatBoost and for SUBXPCA the best accuracy of 98% is achieved by random forest classifier which clearly outperformed the other related works, both in terms of accuracy and computational complexity.

The Electroencephalogram (EEG) is the brain sig- nals which are most normally debased by Electromyogram (EMG) antiquities. The presence of these EMG antiquities covers the necessary information in an EEG signal. In this paper, we have proposed another strategy named as Multi-channel Singular Spectrum Analysis (MSSA) in light of Singular Value Decomposition (SVD) to expel muscle or EMG antiquities from multi-channel EEG signals. At first, the orthogonal eigenvectors of multi-channel data are estimated by performing SVD which are acquired from the covariance matrix . Since the frequency variations of eigenvectors related to EEG signal are quite low when compared to the EMG signal, so we fix some peak frequency threshold to find out the frequencies related to EEG signal, then the frequencies related to EMG signals are suppressed and the artifact free Multi-channel EEG signal is extracted. Finally, our proposed technique is applied on a noisy sinusoidal signals to test the performance of the proposed method and then it is applied on synthetic EEG signals mixed with the EMG artifacts. Simulation results are then compared with Canonical Correlation Analysis (CCA) to show that the proposed method eliminates EMG antiquities more adequately without amending the required data.