Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

Martins, Mónica Cristina Monteiro; Gomes, Pedro; Oliveira, Cristina; Coimbra, Miguel; Silva, Hugo Plácido da

doi:10.1109/tbme.2019.2913913

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…An adequate electrical contact between the skin of the subject and the electrode is, therefore, of paramount importance. However, the electrical contact can be compromised by natural barriers such as androgenic hair [15,16], which is a particularly relevant problem in the context of our work. For benchmarking purposes, we use the PLA Electrode with the hemispherical texture (Figure 2a), as it achieved the best results in [5].…”

Section: Electrode Texturementioning

confidence: 99%

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Silva

Correia

Melo

et al. 2022

Sensors

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This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24–70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 ± 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 ± 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

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Section: Electrode Texturementioning

confidence: 99%

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Silva

Correia

Melo

et al. 2022

Sensors

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“…Accordingly, a frequency response analysis is necessary to get to understand our electronic-stethoscope design. Changing the material of the head of the stethoscope [ 31 ] and adding/removing tubes will impact the resonance frequency [ 32 ]. Even a non-air-tight system impacts the frequency response, which is challenging to handle in a prototype.…”

Section: Background and Related Workmentioning

confidence: 99%

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Bello

Zhou

Lukowicz

2020

Sensors

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Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).

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Multi-modal cardiac function signals classification algorithm based on improved D-S evidence theory

et al. 2022

Biomedical Signal Processing and Control

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Design and Evaluation of a Diaphragm for Electrocardiography in Electronic Stethoscopes

Cited by 8 publications

References 14 publications

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Multi-modal cardiac function signals classification algorithm based on improved D-S evidence theory

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