CardioSounds

Blanco, Andrea Lorena Aldana; Grautoff, Steffen; Hermann, Thomas

doi:10.1145/3123514.3123542

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Other ECG monitoring systems mainly focus on the interpretation of diagnostic algorithms [88,166,169,170]. Arrhythmia diagnosis is one of the most common diseases diagnosed based on ECG signals [171][172][173][174]. Real-time Cloud-based ECG monitoring systems for arrhythmia detection were proposed in [175,176], whereas AF is commonly diagnosed based on ECG signals [57,67,[177][178][179].…”

Section: Service-based Monitoring Systemsmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

230

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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Section: Service-based Monitoring Systemsmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

230

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“…In terms of ECG sonification, there are two main research lines that can be found in literature. Firstly, there are approaches focused on sonifying temporal features such as the heart rate [22] and the heart rate variability 1 [3,4]. Heart rate sonification is used mainly to track performance in sports as the intensity of training can be estimated if a certain heart rate target is reached whereas heart rate variability is tightly related to diagnostics since it is an important estimator for arrhythmias 2 and other heart-related diseases.…”

Section: Related Workmentioning

confidence: 99%

“…Preparation of data and ECG features extractionIn this section, we first explain how we created the ECG signals for the study and then go over the feature extraction 3. From the endocardium to epicardium.…”

mentioning

confidence: 99%

ECG sonification to support the diagnosis and monitoring of myocardial infarction

Blanco

Grautoff

Hermann

2020

J Multimodal User Interfaces

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This paper presents the design and evaluation of four sonification methods to support monitoring and diagnosis in Electrocardiography (ECG). In particular we focus on an ECG abnormality called ST-elevation which is an important indicator of a myocardial infarction. Since myocardial infarction represents a life-threatening condition it is of essential value to detect an ST-elevation as early as possible. As part of the evaluated sound designs, we propose two novel sonifications: (i) Polarity sonification, a continuous parameter-mapping sonification using a formant synthesizer and (ii) Stethoscope sonification, a combination of the ECG signal and a stethoscope recording. The other two designs, (iii) the water ambience sonification and the (iv) morph sonification, were presented in our previous work about ECG sonification (Aldana Blanco AL, Steffen G, Thomas H (2016) In: Proceedings of Interactive Sonification Workshop (ISon). Bielefeld, Germany). The study evaluates three components across the proposed sonifications (1) detection performance, meaning if participants are able to detect a transition from healthy to unhealthy states, (2) classification accuracy, that evaluates if participants can accurately classify the severity of the pathology, and (3) aesthetics and usability (pleasantness, informativeness and long-term listening). The study results show that the polarity design had the highest accuracy rates in the detection task whereas the stethoscope sonification obtained the better score in the classification assignment. Concerning aesthetics, the water ambience sonification was regarded as the most pleasant. Furthermore, we found a significant difference between sound/music experts and non-experts in terms of the error rates obtained in the detection task using the morph sonification and also in the classification task using the stethoscope sonification. Overall, the group of experts obtained lower error rates than the group of non-experts, which means that further training could improve accuracy rates and, particularly for designs that rely mainly on pitch variations, additional training is needed in the non-experts group.

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“…An important application was highlighted in aiding the training of athletes who were able to maintain stable target heart rates by using sonification-assisted visual monitoring of HRV [ 23 , 24 ]. Aldana Blanco et al [ 28 ] presented the CardioSounds system, embedding two methods for HRV and ST-segment sonification as auditory feedback to support sports activities or monitoring ST segment changes related to myocardial infarction. Warnings of ST-segment depression above attention and alert thresholds were sonified with cascaded sounds of varying durations [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…Studies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ] demonstrated that ECG sonification has the potential to offer real-time monitoring capabilities, enabling individuals to hear irregularities or anomalies in ECG patterns without the need for constant visual attention. Also, humans do not even have to be focused to react to sound (acoustic) alerts.…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

Krasteva,

Iliev,

Tabakov

2024

Sensors

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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.

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CardioSounds

Cited by 5 publications

References 4 publications

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

ECG sonification to support the diagnosis and monitoring of myocardial infarction

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)

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