DENS-ECG: A Deep Learning Approach for ECG Signal Delineation

Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of different heartbeat waveforms such as the P-wave, QRS complex, T-wave, and No wave (NW). Using ECG as the inputs, the model learns to extract high level features through the training process, which, unlike other classical machine learning based methods, eliminates the feature engineering step. Results: The proposed DENS-ECG model was trained and validated on a dataset with 105 ECGs of length 15 minutes each and achieved an average sensitivity and precision of 97.95% and 95.68%, respectively, using a 5-fold cross validation. Additionally, the model was evaluated on an unseen dataset to examine its robustness in QRS detection, which resulted in a sensitivity of 99.61% and precision of 99.52%. Conclusion: The empirical results show the flexibility and accuracy of the combined CNN-LSTM model for ECG signal delineation. Significance: This paper proposes an efficient and easy to use approach using deep learning for heartbeat segmentation, which could potentially be used in real-time tele-health monitoring systems

Intelligent condition assessment of power transformers

This thesis begins by providing an introduction to different transformer failures and the most effective condition monitoring techniques. Different failures are introduced and their corresponding fault diagnosis methods are listed to have a better understanding of failure modes and their consequence effects. An investigation into monitoring major failures of transformers using dissolved gas analysis is then presented. Various conventional, dissolved gas analysis based, fault diagnosis techniques are presented and the drawbacks of these methods are discussed. Intelligent fault diagnosis methods are introduced to overcome the problems of the conventional techniques. An overview of statistical and machine learning algorithms applied in this research is also described. Preliminary research results on transformer load tap changers fault classification are reported. A hierarchical fault diagnosis algorithm for transformer load tap changers using support vector machines is used, in which, for each fault class, a unique single support vector machine algorithm is employed. However, while the developed algorithm is reasonably accurate, the shortcomings of applying single learning algorithms are discussed and a proposal for developing a more robust and generalised transformers condition assessment algorithm is made. An intelligent power transformer fault diagnosis algorithm is then developed to classify faults of transformers. The proposed fault diagnosis algorithm is an ensemble-based approach which uses different statistical and machine learning algorithms. In the first phase of the proposed algorithm the most relevant features (dissolved gases) corresponding to each fault class are first determined. Then, selected features are used to classify transformer faults. The results of this algorithm show a significant improvement, in terms of classification. A time-series forecasting algorithm is developed to predict future values of dissolved gases in transformers. The dataset for this algorithm was collected from a transformer for a period of six months which consisted of seven dissolved gases, a loading history, and three measured, ambient, oil, and winding, temperatures of transformer. The correlation coefficients between these 11 time series are then calculated and a nonlinear principle component analysis is used to extract an effective time series from highly correlated variables. The proposed multi-objective evolutionary time series forecasting algorithm selects the most accurate and diverse group of forecasting methods among various implemented time series forecasting algorithms. The proposed method is also compared with other conventional time series forecasting algorithms and the results show the improvements over the different forecasting horizons

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer’s disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer’s disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities