A Novel Method for ECG Signal Classification Via One-Dimensional Convolutional Neural Network

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Advances in Bearing Lubrication and Thermal Sciences

This reprint focuses on the hot issue of bearing lubrication and thermal analysis, and brings together many cutting-edge studies, such as bearing multi-body dynamics, bearing tribology, new lubrication and heat dissipation structures, bearing self-lubricating materials, thermal analysis of bearing assembly process, bearing service state prediction, etc. The purpose of this reprint is to explore recent developments in bearing thermal mechanisms and lubrication technology, as well as the impact of bearing operating parameters on their lubrication performance and thermal behavior

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Algorithms for Compression of Electrocardiogram Signals

The study is dedicated to modern methods and algorithms for compression of electrocardiogram (ECG) signals. In its original part, two lossy compression algorithms based on a combination of linear transforms are proposed. These algorithms are with relatively low computational complexity, making them applicable for implementation in low power designs such as mobile devices or embedded systems. Since the algorithms do not provide perfect signal reconstruction, they would find application in ECG monitoring systems rather than those intended for precision medical diagnosis. This monograph consists of abstract, preface, five chapters and conclusion. The chapters are as follows: Chapter 1 — Introduction to ECG; Chapter 2 — Overview of the existing methods and algorithms for ECG compression; Chapter 3 — ECG compression algorithm, based on a combination of linear transforms; Chapter 4 — Improvement of the developed algorithm for ECG compression; Chapter 5 — Experimental investigations. Този труд е посветен на съвременните методи и алгоритми за компресия на електрокардиографски (ЕКГ) сигнали. В оригиналната му част са предложени два алгоритъма за компресия със загуби, които са базирани на комбинация от линейни преобразувания. Тези алгоритми се характеризират със сравнително невисока изчислителна сложност, което дава възможност да бъдат реализирани в устройства с ниска консумация на енергия, като например мобилни устройства или вградени системи. Тъй като алгоритмите не позволяват перфектно възстановяване на сигнала, те биха намерили приложение по-скоро в системите за ЕКГ мониторинг, отколкото в тези, предназначени за прецизна медицинска диагностика. Монографията съдържа резюме, предговор, пет глави и заключение. Главите са както следва: Глава 1 — Въведение в електрокардиографията; Глава 2 — Обзор на съществуващите методи и алгоритми за компресия на ЕКГ сигнали; Глава 3 — Алгоритъм за компресия на ЕКГ сигнали, базиран на комбинация от линейни преобразувания; Глава 4 — Усъвършенстване на разработения алгоритъм за компресия на ЕКГ сигнали; Глава 5 — Експериментални изследвания

Computing Intelligence Technique and Multiresolution Data Processing for Condition Monitoring

Condition monitoring (CM) of rotary machines has gained increasing importance and extensive research in recent years. Due to the rapid growth of data volume, automated data processing is necessary in order to deal with massive data efficiently to produce timely and accurate diagnostic results. Artificial intelligence (AI) and adaptive data processing approaches can be promising solutions to the challenge of large data volume. Unfortunately, the majority of AI-based techniques in CM have been developed for only the post-processing (classification) stage, whereas the critical tasks including feature extraction and selection are still manually processed, which often require considerable time and efforts but also yield a performance depending on prior knowledge and diagnostic expertise. To achieve an automatic data processing, the research of this PhD project provides an integrated framework with two main approaches. Firstly, it focuses on extending AI techniques in all phases, including feature extraction by applying Componential Coding Neural Network (CCNN) which has been found to have unique properties of being trained through unsupervised learning, capable of dealing with raw datasets, translation invariance and high computational efficiency. These advantages of CCNN make it particularly suitable for automated analyzing of the vibration data arisen from typical machine components such as the rolling element bearings which exhibit periodic phenomena with high non-stationary and strong noise contamination. Then, once an anomaly is detected, a further analysis technique to identify the fault is proposed using a multiresolution data analysis approach based on Double-Density Discrete Wavelet Transform (DD-DWT) which was grounded on over-sampled filter banks with smooth tight frames. This makes it nearly shift-invariant which is important for extracting non-stationary periodical peaks. Also, in order to denoise and enhance the diagnostic features, a novel level-dependant adaptive thresholding method based on harmonic to signal ratio (HSR) is developed and implemented on the selected wavelet coefficients. This method has been developed to be a semi-automated (adaptive) approach to facilitate the process of fault diagnosis. The developed framework has been evaluated using both simulated and measured datasets from typical healthy and defective tapered roller bearings which are critical parts of all rotating machines. The results have demonstrated that the CCNN is a robust technique for early fault detection, and also showed that adaptive DD-DWT is a robust technique for diagnosing the faults induced to test bearings. The developed framework has achieved multi-objectives of high detection sensitivity, reliable diagnosis and minimized computing complexity