Fuzzy C-Means Clustering and Sonification of HRV Features

Borthakur, Debanjan; Grace, Victoria; Batchelor, Paul; Dubey, Harishchandra; Mankodiya, Kunal

doi:10.1109/chase48038.2019.00024

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…The stimuli featured sequential trials of stressor, guided breathing, and unguided breathing sections ( Figure 1). The stimuli were procedurally generated by Muvik Labs' Augmented Sound™ engine to ensure timing precision and effectiveness through evidence-backed breathing interventions driven by principles of psychoacoustics and behavioral psychology [64].…”

Section: Participants and Data Acquisitionmentioning

confidence: 99%

“…Following an initial EEG baseline recording for 120 sec ('Baseline'), participants performed a 25-min session featuring stress induction and breath modulation tasks consisting of four main blocks. Each block began with a stressor ('Stressor') featuring an augmented arithmetic number task, intensified by bright contrasting colors displaying numbers appearing sequentially, coupled with audible sonified timers mapped to rising pitches similar to Shepard tones (powered by Muvik Labs Augmented Sound™) [64], with a 90 second time constraint. Timed mental arithmetic has been widely used to induce stress [67][68].…”

Section: Stress Induction and Alleviationmentioning

confidence: 99%

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Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

Penchina¹,

Sundaresan²,

Cheong³

et al. 2020

Preprint

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Anxiety is one of the most common comorbidities in youth with autism spectrum disorder (ASD), severely limiting academic opportunities and overall quality of life. In the present study, we compared several machine learning classifiers, namely support vector machine (SVM) and deep learning methods, in order to evaluate the feasibility of an EEG-based BCI for the real-time assessment and mitigation of anxiety through a closed-loop adaptation of respiration entrainment. We trained a total of eleven subject-dependent models—four with conventional BCI methods and seven with deep learning approaches—on the EEG of neurotypical (n=5) and ASD (n=8) participants performing alternating blocks of mental arithmetic stress induction, guided and unguided breathing. We propose a multiclass two-layer LSTM RNN deep learning classifier capable of identifying anxious states from ongoing EEG with an overall accuracy of 93.27%. Our study is the first to successfully apply an LSTM RNN classifier to identify anxious states from EEG in both neurotypical adolescents and adolescents with ASD.

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Section: Participants and Data Acquisitionmentioning

confidence: 99%

Section: Stress Induction and Alleviationmentioning

confidence: 99%

Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

Penchina¹,

Sundaresan²,

Cheong³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, higher HRV in atrial fibrillation could be heard as a discontinuous jump between high and low pitches with relatively constant tinkling sounds. Further studies investigated the appropriate HRV features to improve the perceptual quality and sonification interpretability [ 23 , 24 , 25 , 26 , 27 ]. 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 ].…”

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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Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism

Penchina¹,

Sundaresan²,

Cheong³

et al. 2020

Lecture Notes in Computer Science

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Fuzzy C-Means Clustering and Sonification of HRV Features

Cited by 7 publications

References 15 publications

Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

Evaluating Deep Learning EEG-Based Anxiety Classification in Adolescents with Autism for Breathing Entrainment BCI

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

Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism

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