Design and Implementation of an Algorithm for Cardiac Pathologies Detection on Mobile Phone

Abstract

The development and the design of telemedicine services have taken a great consideration and care in the domain of wireless communication nowadays. The set of these researches is concerned with old people and lack of infrastructures of reception for those who are at risk or tend to have deterioration in their health condition. Thus, several works of research contributed to develop telemedicine services. They notably focus on the conception and the development of communication architectures between the actors of these systems, monitoring and the development of human's quality is based on the storage of the collected data at home and analytical tools, and processing of these large quantities of data. Therefore, it is useful to detect and prevent the occurrence of critical situations of a remote person, the transmission of the messages and alarms to concerned actors to be ready to intervene in a case of emergency. Many works and systems undertaken in this field carry out the complete analysis and synthesis of signals on large servers (great capacities, better resolutions…). Moreover, these systems would have required large means and a large infrastructure in their deployment (installation, configuration…), which generates the disadvantage of the excessive expenditure. In this paper, we suggest to introduce and implement this complete treatment for revealing critical situations and pathologies on a simple mobile phone by respecting theirs constraints. The principal objective is to permit a taking off for medical and social dependant people as aged ones, handicapped, in order to the adaptation with their environment domestically and make up their incapacities. In this case, it is indispensable to make a diagnostic in a real time and well manage the patient's computerized data between the various medical actors with the permanent security insurance of highly risky patients. Furthermore, the need to make a speed diagnostic of patients and to detect their health state, their parameters (medical information) of analyses with efficacy, allows us to gain time while monitoring the cardiac patient. It concerns the implementation of services on mobile terminals for transferring medical information and results of ECG analysis (calculated parameters) in a real time with ensuring the mobility, the permanent security and the reliability insurance in covered zone by the mobile network, PLMN (GSM/GPRS…). Our attention has been focused on the choice of a relevant work. It concerns an application on a mobile terminal (MIDlet) for detecting some cardiac pathologies and monitoring patient in a nonhospital setting. This paper recalls a complete architecture of an economic wireless transmission system with the implementation of an effective algorithm, adapted to the mobile terminal, allowing to the doctor to have the results of the ECG analysis. Thus, the stakes of setting up such systems are numerous, so much for patients, medical staff and the society in general.

A Review on Remote Monitoring System for Moving Patients

“…(b) low cost online acquisition of ECG signal using MATLAB, LabView and time plane feature extraction from digitalized ECG samples using a statistical approach [12]. (c) a mobile based ECG detection and analysis algorithm [14] (d) ARTIFACT, a software tool for processing ECG data [13] (e) ECG signal processing and digital filtering on 8-bit microcontroller [15] (f) mean shift based self-adaptive model [16] Between a fixed Base station and the mobile stations wireless communication takes place within a coverage area with an acceptable signal to noise ratio. The implementation complexities of the mobile station receiver are reduced, and the power consumption in the mobile terminal can be decreased.…”

Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges

“…Falls prevention and detection and biofeedback monitoring systems using ubiquitous devices Sclafani et al 27 Various MATLAB TM and LabVIEW TM and time-plane feature extraction from digitized ECG samples that uses a statistical approach, 45 a mobile-based physiological parameter detection and analysis algorithm 46 ARTiiFACT, a tool for collecting real-time ECG data from integrated sensors and mobile platform to directly collect and analyze the data, 47 ECG signal processing and digital filtering on 8-bit microcontroller, 48 and a mean shift-based self-adaptive model. 49 Personalized and predictive medicine can greatly benefit from data integration and its machine learning model (big data analytics).…”

“…41 Various techniques have been applied to collect high-quality health data. 7,42,43 Examples include a novel unbiased and normalized adaptive noise reduction application to suppress random noise in ECG signals, which includes two-stage moving-average filter, an infinite impulse response comb filter, an additive white noise generator to test the system’s performance in terms of signal-to-noise ratio, 44 a low-cost online acquisition of ECG signal using MATLAB TM and LabVIEW TM and time-plane feature extraction from digitized ECG samples that uses a statistical approach, 45 a mobile-based physiological parameter detection and analysis algorithm 46 ARTiiFACT, a tool for collecting real-time ECG data from integrated sensors and mobile platform to directly collect and analyze the data, 47 ECG signal processing and digital filtering on 8-bit microcontroller, 48 and a mean shift-based self-adaptive model. 49…”

Adaptation of an Intelligent Mobile Assistant Medical (IMAM) of the Heterogeneous Data for the Telemedicine Services: Design and Implementation

“…When it concerns a treatment or calculation, they converted to the numbers format just to carry out the arithmetic operations. This quantity of collected data, allow us to obtain relevant results especially in the case of the classification of ECG signals [11].…”