Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming

Arrhythmia classification detection based on multiple electrocardiograms databases

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

BACKGROUND AND OBJECTIVE

Automated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs.

METHODS

Inspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject-oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while following a benchmark scheme. By adopting various mother wavelets, multiple WavelNet-based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN- and SincNet-based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro+ environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation.

RESULTS

The proposed WavelNet-based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spectral analysis while preserving temporal ECG information compared with vanilla CNN- and SincNet-based models. In particular, a Symlet 4 wavelet-adopting WavelNet-based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat classifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies.

CONCLUSIONS

Our proposed WavelNet-based arrhythmia classification model achieved remarkable performance based on a reasonable decision-making process, in comparison with other models. As its noteworthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases.

Framing Apache Spark in life sciences

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.

Symptom-based analysis of COVID-19 cases using supervised machine learning approaches to improve medical decision-making

The world today faces a new challenge that is unprecedented in the last 100 years. The emergence of a new coronavirus has led to a human catastrophe. Scientists in various sciences have been looking for solutions to this problem so far. In addition to general vaccination, maintaining social distance and adherence to government guidelines on safety precaution measures are the most well-known strategies to prevent COVID-19 infection. In this research, we tried to examine the symptoms of COVID-19 cases through different supervised machine learning methods. We solved the class imbalance problem using the synthetic minority over-sampling (SMOTE) method and then developed some classification models to predict the outcome of COVID-19 cases (recovery or death). Besides, we implemented a rule-based technique to identify different combinations of variables with specific ranges of their values that together affect disease severity. Our results showed that the random forest model with 95.6% accuracy, 97.1% sensitivity, 94.0% specification, 94.4% precision, 95.8% F-score, and 99.3% AUC-score outperforms state-of-the-art classification models. Finally, we identified the most significant rules that state various combinations of 6 features in certain ranges of their values lead to patients’ recovery with a confidence value of 90%. In conclusion, the classification results in this study show better performance than recent studies, and the extracted rules help physicians consider other important factors to improve health services and medical decision-making for different groups of COVID-19 patients.