Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment.

Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.

Prediction of Heart Disease Using Deep Convolutional Neural Networks

The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

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Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

The accurate electrocardiogram (ECG) interpretation is important for several potentially life-threatening cardiac diseases. Recently developed deep learning methods show their ability to distinguish some severe heart diseases. However, since deep neural network requires a high cost on memory consumption and computation, implementation scenarios of these interpretation methods are constrained to nonportable devices. Few commercial portable devices only have heartbeat detection ability, and therefore, only a few simple cardiac diseases can be diagnosed. In this article, to achieve diagnosing a wide range of cardiac diseases and continuous monitoring, a homecare-oriented ECG diagnosis platform is designed based on a large-scale multilabel deep conventional neural network. The accuracy of the proposed neural network model is guaranteed by our constructed large-scale ECG dataset, which is comprised of 206 468 standard 12-lead ECG recordings from 89 488 patients, with respect to 26 types of most common heart rhythms and conduction abnormalities. Meanwhile, targeting lightweight homecare or wearable applications, algorithm-hardware co-optimization is conducted to accelerate the model computation on an embedded platform with field-programmable gate array (FPGA) for continuous monitoring. Channel-level pruning and parameters quantization strategy are employed to optimize the network, and a reconfigurable accelerator hardware architecture is designed to accelerate the convolution computation on FPGA. The final quantified model achieved a promising $F_{1}$ score of 0.913% and 86.7% exact match ratio, in which parameters and floating-point operations per second (FLOPs) are significantly penalized compared to the original large-scale model. Real-time analysis is performed. Specifically, the average processing time for each ECG record is 2.895 s, and it can be applied to homecare or portable ECG diagnosis devices for continuous monitoring.

Homecare-Oriented ECG Diagnosis With Large-Scale Deep Neural Network for Continuous Monitoring on Embedded Devices

Heart disease, also known as cardiovascular disease (CVD), is the foremost among all widespread diseases in the people community. Any disorder that affects the heart's function is typically called heart disease. Narrowing or blockage of the coronary arteries, which supply blood to the heart, is the most common cause of heart failure. Coronary Artery Disease (CAD) is a common form of heart disease and the leading cause of heart attacks. Nowadays, there is no age limit for people to get affected by this disease. There are so many diagnosis methods available where most are costly, the risk involved, and technical experts are needed to perform the disease diagnosis. Clinical research has pointed out different factors that increase the risk of CAD and heart attack. These factors can be categorized into two types, i.e., risk factors that cannot be changed and those that can be changed. Sex, age and family history are those factors that cannot be altered. In contrast, factors related to a subject's lifestyle, e.g., smoking, high cholesterol, high blood pressure and physical inactivity, can be changed. This paper reviews various deep learning techniques involving heart disease prognostic and their accuracy in predicting that they can be treated in advance to prevent fatalities.

Deep learning models on Heart Disease Estimation - A review

Accurate classification of heart diseases plays an important role and IoTs applied in a medical system will increase the effectiveness of diagnosis In this chapter, we propose an IoTs-based diagnostic system for heart diseases classification This system is designed to transmit classified data to server for storage and diagnosis In particular, ECG devices are connected to internet systems through wifi or 3G/4G technologies for transmitting ECG data to a cloud-based processing system for storing patient’s profiles Therefore, datasets are pre-processed for extracting features using a WPD algorithm In addition, a wkPCA method and a deep learning framework are employed for classifying heart diseases Experimental results and the IoTs-based system description are shown to illustrate the effectiveness of the proposed method

A Deep Learning Framework for Heart Disease Classification in an IoTs-Based System

Falling of elderly people is one of main reasons causing serious injuries or the risk of early death. However, this may be reduced by using an IoTs-based fall detection system, in which a SVM algorithm and PCA features are applied. In addition, datasets collected from tri-axial accelerometer sensors and/or Kinect camera systems are transferred to a central Hub via Zigbee interface and are updated continuously to a cloud server for processing and detecting fall states. In addition, fall messages can be sent to relatives through smartphones and/or healthcare centers for alert and supporting soon. Experimental results show to illustrate the effectiveness of the proposed system.

A SVM Algorithm for Falling Detection in an IoTs-Based System

Nowadays, heart disease has very popularly affected human health. Early diagnosis of heart disease using deep learning networks is a significant task due to possible support for physicians. This paper proposed to build a remote diagnostic system for heart disease via a server. In particular, a patient’s ECG signals are collected from a machine and then sent to the server containing a heart disease classification system using the deep learning network for classifying heart diseases. Furthermore, this real system was designed with a suitable ECG data transmission protocol so that physicians can access the ECG signals and classification results using a smartphone or remote computer. The results obtained from the real experiments in the BME Lab showed that the proposed system operated stably and it can be developed to implement in diagnostic centers or hospitals.

Thanh-Tam Nguyen

Papers

A Deep Learning Framework for Heart Disease Classification in an IoTs-Based System

A SVM Algorithm for Falling Detection in an IoTs-Based System

A Remote Diagnostic System using Deep Learning Network for Heart Disease Detection