Ectopic Beat Detection from Wrist Optical Signals for Sinus Rhythm and Atrial Fibrillation Subjects

Haddad, Serj; Harju, Jarkko; Tarniceriu, Adrian; Halkola, Tuomas; Parák, Jakub; Korhonen, Ilkka; Yli‐Hankala, Arvi; Vehkaoja, Antti

doi:10.1007/978-3-030-31635-8_18

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Indeed, we have shown that detecting PAC/PVC can lead to significant false positive reduction during AF detection [ 40 ]. Other recent published reports [ 14 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] have largely focused on AF without accounting for PAC/PVC and consequently their accuracy of AF detection was suboptimal.…”

Section: Discussionmentioning

confidence: 99%

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Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Han

Bashar

Mohagheghian

et al. 2020

Sensors

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We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.

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Section: Discussionmentioning

confidence: 99%

“…Moreover, the dataset used in this paper did not consider PVC arrhythmia. Many other studies on AF detection using wrist PPG sensors were conducted [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32], however, none of them considered PAC or PVC case in their AF detection.…”

Section: Introductionmentioning

confidence: 99%

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Han

Bashar

Mohagheghian

et al. 2020

Sensors

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“…It is deployed in most of the mainstream wearable devices and smart phones. Studies confirm the accuracy of PPG-based solutions to monitor various vital signs, such as heart rate (HR) and heart rate variability (HRV) 8,9 , stress levels 10 , irregularity in heart beats 11,12 , sleep quality 13 , respiratory rate 14 , etc. For BP monitoring, PPG-based methods rely on either pulse wave velocity (PWV) techniques 15,16 or pulse wave analysis (PWA) methods 17,18 .…”

Section: Introductionmentioning

confidence: 80%

The Accuracy of Blood Pressure Monitoring Using the Senbiosys Ring: A Study on Patients Undergoing Coronary Angiography and Patients in the Intensive Care Unit

Haddad¹,

Schukraft

Boukhayma³

et al. 2021

Preprint

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The study NCT04379986 aims at evaluating the performance of the Senbiosys technology in monitoring blood pressure (BP) values for elderly patients: 25 patients undergoing coronary angiography, aged 68.92 ± 6.41 years, and 10 patients in the intensive care unit (ICU), aged 71.90 ± 8.56 years. The technology is based on pulse wave analysis (PWA) of low-power photoplethysmography (PPG) from the SBF2003 ring. The PPG recordings are processed by the PPG-based blood pressure monitoring (PPG-BPM) algorithm 1 to generate systolic BP (SBP) and diastolic BP (DBP) estimates. For the SBP, the mean difference ± standard deviation between the reference arterial line and the PPG-BPM values is 0 ± 5.06 mmHg for the patients undergoing coronary angiography and 0 ± 7.46 mmHg for the patients in the ICU. For the DBP, the mean difference ± standard deviation between the reference arterial line and the PPG-BPM values is 0 ± 1.85 mmHg for the patients undergoing coronary angiography and 0 ± 4.17 mmHg for the patients in the ICU. The results show that the accuracy of our algorithm is within the 5 ± 8 mmHg ISO/ANSI/AAMI protocol requirement.

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“…Since the outbreak of the COVID-19 pandemic the HRV data is becoming very crucial and is being heavily analyzed to study the effect of the COVID-19 on the mental health and the stress levels of individuals. HRV is also applied for sleep monitoring [29], and different types of arrhythmia detection [30], [31]. Therefore, it goes without saying that accurate, reliable, and continuous HRV monitoring is of utmost importance.…”

Section: A Importance Of Accurate Beat Detectionmentioning

confidence: 99%

Ring-Embedded Micro-Power mm-Sized Optical Sensor for Accurate Heart Beat Monitoring

Boukhayma¹,

Barison²,

Haddad³

et al. 2021

IEEE Access

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This paper presents a wearable optical health monitoring device embedding a monolithic sensor chip with newly designed pixels, and an optical module optimized for photoplethysmography (PPG). The monolithic optical sensor at the heart of this device implements an array of novel pixels designed specifically for PPG and featuring a quantum efficiency (QE) as high as 85 %. The sensor readout chain, as well as the logic circuits, implements low power design techniques leading to only 60 µA current consumption at 122 Hz operation frequency, including digital communication. Thanks to the high QE and optical module optimization, medical grade PPG recordings are enabled with less than 10 µA emitter current. The optical sensor is embedded in an ergonomic ring device together with system electronics in order to enable in-field test of beat detection accuracy on 7 subjects. The ring device detects correctly 97.87 % of the beats on a total of 72.21 hours of recording. The inter-beat interval (IBI) estimation from these recordings features a mean absolute error (MAE) as low as 8.10 ms. This performance is achieved with less than 70 µA current consumption at the level of the PPG module.INDEX TERMS CMOS image sensor, heart beat detection, low power integrated circuit, monolithic optical detector, photoplethismography sensor, wearable electronics.

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Ectopic Beat Detection from Wrist Optical Signals for Sinus Rhythm and Atrial Fibrillation Subjects

Cited by 9 publications

References 10 publications

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

The Accuracy of Blood Pressure Monitoring Using the Senbiosys Ring: A Study on Patients Undergoing Coronary Angiography and Patients in the Intensive Care Unit

Ring-Embedded Micro-Power mm-Sized Optical Sensor for Accurate Heart Beat Monitoring

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