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СКАЧАТЬ be diagnosed with deep learning algorithms. An autonomous interpretation of echocardiography was successfully implemented through a deep learning algorithm by Zhang et al. [20]. Using the data collection of 14,000 samples, the algorithm could successfully distinguish between broad echocardiographic view classes (e.g., short axis and parasternal long axis) with an accuracy of 96%.

      Automated quantification of cardiac structure with the help of Convolutional Neural Networks (CNNs) was superior to manual measurements. Moreover, authors have trained the CNN to detect pulmonary artery hypertension, cardiac amyloidosis, and hypertrophic cardiomyopathy with high accuracy. These evolutions prove that the idea of embedding AI techniques in mHealth devices has become a reality. Machine learning and deep learning techniques are an effective means of handling the sheer complexity of the data. While comparing with other disciplines, cardiologists have numerous amounts of data at their disposal. Since the data complexity grows, it is important for an AI technique to be embedded in the clinical practice. So, it is expected in the future that all cardiologists to be data scientists and physicians simultaneously.

The summary of the above discussed articles on AI-driven mHealth communication is listed in Table 1.3.

1.4 AI-Driven Body Area Network Communication Technologies and Applications

      The development and growth of wireless sensor networks play a vital role in the field of medical and health servicing sectors. In modern technology, wireless communication provides a lot of possibilities for the sharing of information at anytime and anywhere. The main objective of this chapter is to explore body area network communication technologies driven by AI. Medical AI mainly utilizes computer network topologies to perform monitoring, recording, diagnoses, and treatment process [21].

Table 1.3 Impact of AI-mHealth communication system in healthcare.

Source	Subject matter	Role of AI-Driven mHealth devices	Related performance measures
[12–14]	Medical big data analysis	Personalized clinical decision-making	A complex diagnosis process in multiple chronic illnesses became simplerSimilarities in illness patterns are analyzed effectively
[15]	Digital healthcare	Health Monitoring - Continuous Glucose Monitoring (CGM)CardioMEMS Heart Sensor with Wireless implantable Hemodynamic Monitoring (W-HM)Automated diagnostic algorithm	POCUS uses in heart diseasesCGM early detects hypoglycemic episodesW-HM results in 30% reduction in heart failure readmissions (hazard ratio 0.70, 95% confidence interval 0.60–0.84)
[16]	Atrial fibrillation detection	C statistic–based trained ANN using smart watch data	ANN predicts AF with 90.2% specificity and 98% sensitivity
[17]	Echocardiographic evaluation	Machine learning–based Associative memory classifier	Achieves 22% more accuracy in prediction than SVM.
[18, 19]	Transthoracic 3D Echocardiography (TTE) Left Heart Chamber Quantification	Automated Adaptive Analytics Algorithm	Achieves better correlation (r = 0.87 to 0.96) with manual 3D TTE
[20]	Echocardiogram Interpretation	CNN-based detection trained with 14 035 Echocardiogram images	CNN detects hypertrophic cardiomyopathy, cardiac amyloidosis, and pulmonary arterial hypertension with 95% accuracy.

A body area network has wide applications in medical and non-medical fields. In the medical field, they are either used as wearable devices or implanted in a patient’s body or as a remote monitoring system to keep track of patient’s health based on the sensory nodes positioned in their bodies. This is very sensitive to older adults or patients with chronic diseases. Through biomedical sensors, motion detectors, and wireless communication, monitoring of every activity like glucose, blood pressure, and pulse rate is done. Figure 1.5 shows a typical body area network with wearable devices for health monitoring. All the required information is collected through the central hub and processed wirelessly to the healthcare provider or medical staff during emergencies. The end devices can also be wearable [22–24], which act as transducers to display human activities, temperature, and pressure.

      Communication in the body sensor network is of two types.

      1 (i) In-body communication uses RF signals between sensory nodes, which are implanted in our human body. The frequency at which the communication has to take place is defined by Medical Implantable Communication Service (MICS), and the range of frequency is 402–405 MHz

      2 (ii) On-body communication is the communication between wearable sensory nodes, which consists of biosensors. Ultrawideband (UWB) can be used for on-body communication. IMS based, which is mainly used for industrial, medical, and scientific applications having a range of 2.4–2.485 GHz. Many electronic applications operate on this band.

Figure 1.5 Wearable devices in the health monitoring system (Adopted from [21]).

      1.4.1 Features

      Since the nodes are placed inside and outside the human body, it requires less power consumption as the devices are battery operated. So, it is essential that for the battery to work longer, power consumption should be less. As communication deals with bio-signals in the medical field, the Quality of Service (QoS) plays an important role. So, the user can detect proper information and treat accordingly.

      As the network deals with information transmission related to vital parameters of human beings, the security of data is critical to avoid unauthorized accessibility. In the case of biosensors, the threshold value is set. So, if any parameter increases or decreases below the threshold value, then it generates an alarm, so fewer false alarms are required. Wireless Medical Telemetry Service (WMTS) and UWB are technologies that are used for body monitoring systems because of their low transmission power.

      1.4.2 Communication Architecture of Wireless Body Area Networks

In this section, we discuss the architecture of WBAN, which is divided into the three-stage process to depict the working mechanism of WBAN as shown in Figure 1.6 [25].

      Stage 1: Intra sensor communication

      The СКАЧАТЬ