RESUMEN
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Global System for Mobile Communications) microphones. Thus, the wireless connection between the patient module and the cloud server can be provided over an audio channel, such as a standard telephone call or audio message. Patients, especially the elderly or visually impaired, can benefit from ECG sonification because the wireless interface is readily available, facilitating the communication and transmission of secure ECG data from the patient monitoring device to the remote server. The aim of this study is to develop an AI-driven algorithm for 12-lead ECG sonification to support diagnostic reliability in the signal processing chain of the audio ECG stream. Our methods present the design of two algorithms: (1) a transformer (ECG-to-Audio) based on the frequency modulation (FM) of eight independent ECG leads in the very low frequency band (300-2700 Hz); and (2) a transformer (Audio-to-ECG) based on a four-layer 1D convolutional neural network (CNN) to decode the audio ECG stream (10 s @ 11 kHz) to the original eight-lead ECG (10 s @ 250 Hz). The CNN model is trained in unsupervised regression mode, searching for the minimum error between the transformed and original ECG signals. The results are reported using the PTB-XL 12-lead ECG database (21,837 recordings), split 50:50 for training and test. The quality of FM-modulated ECG audio is monitored by short-time Fourier transform, and examples are illustrated in this paper and supplementary audio files. The errors of the reconstructed ECG are estimated by a popular ECG diagnostic toolbox. They are substantially low in all ECG leads: amplitude error (quartile range RMSE = 3-7 µV, PRD = 2-5.2%), QRS detector (Se, PPV > 99.7%), P-QRS-T fiducial points' time deviation (<2 ms). Low errors generalized across diverse patients and arrhythmias are a testament to the efficacy of the developments. They support 12-lead ECG sonification as a wireless interface to provide reliable data for diagnostic measurements by automated tools or medical experts.
Asunto(s)
Redes Neurales de la Computación , Ríos , Humanos , Anciano , Reproducibilidad de los Resultados , Electrocardiografía/métodos , Algoritmos , Procesamiento de Señales Asistido por ComputadorRESUMEN
Long-term remote patients monitoring implies minimal discomfort and reliability throughout the study period. These requirements are fulfilled by portable (wearable) patient devices, with low consumption, which transmit data wirelessly, at a short distance, to a mobile communication device (GSM) and through it, to a remote end recipient - doctor, medical center or a hospital server. The data transfer technology requires the monitored person to perform a sequence of actions, such as: selecting the appropriate application on the mobile phone, establishing a connection between the patient module and the phone, recording the data in the phone's memory, starting the data transfer from the phone to the final receiver. Practice shows that often this sequence of activities is difficult for elderly people and especially for visually impaired people, which as a result compromises the remote monitoring process. In this paper are presented an approach and conceptual implementation of a system for remote monitoring of cardiac activity, using the most popular way of remote connectivity - voice (sound) communication. In addition to the ease of use, this type of communication does not require special data protection, due to the lack of RF interfaces for short-distance data transmission. The presented results of laboratory studies, as well as conducted tests under medical supervision of patients in a cardiology clinic, confirm the workability of the proposed approach for remote monitoring of patients by audio conversion of the ECG signal.