scholarly journals Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6017
Author(s):  
Martin Glasstetter ◽  
Sebastian Böttcher ◽  
Nicolas Zabler ◽  
Nino Epitashvili ◽  
Matthias Dümpelmann ◽  
...  
Keyword(s):  
Heart Rate ◽  
Seizure Detection ◽  
Motion Artifacts ◽  
High Signal ◽  
Signal Quality ◽  
Spectral Entropy ◽  
Average Delay ◽  
Focal Seizures ◽  
Early Manifestation

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.

scholarly journals Design rationale and performance evaluation of Wavelet Health Wristband: bench-top validation of a wrist-worn physiological signal recorder (Preprint)

2018 ◽  
Author(s):  
Onur Dur ◽  
Colleen Rhoades ◽  
Sally Man Suen Ng ◽  
Ragwa Elsayed ◽  
Reinier van Mourik ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Respiration Rate ◽  
Vital Signs ◽  
High Signal ◽  
Signal Quality ◽  
Absolute Deviation ◽  
Physiological Signal ◽  
And Performance

BACKGROUND Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient and scalable way to collect personal health data remotely. The Wavelet Health Platform and the Wavelet Wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals including resting heart rate, heart rate variability, and respiration rate. OBJECTIVE This study aims to evaluate the accuracy of the biometrics estimates and signal quality of the wristband. METHODS Measurements collected from 35 subjects using the Wavelet Wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements. RESULTS The heart rate, heart rate variability (SDNN) and respiration rate estimates matched within 0.6 ± 0.9 bpm, 7 ± 10 ms and 1 ± 1 brpm mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable to that obtained from measurements from a finger-clip plethysmography device. CONCLUSIONS The accuracy of the biometrics estimates and high signal quality indicate that the Wristband PPG device is suitable for performing pulse wave analysis and measuring vital signs.

DEVELOPMENT OF A DEVICE FOR CONTROL OF THE STATE OF THE ORGANISM BASED ON PHOTOPLETISMOGRAPHY USING TECHNOLOGIES OF DIGITAL ADAPTIVE FILTRATION

2020 ◽  
pp. 100-107
Author(s):  
Алексей Дмитриевич Акишин ◽  
Иван Павлович Семчук ◽  
Александр Петрович Николаев
Keyword(s):  
Heart Rate ◽  
Cardiovascular System ◽  
Adaptive Filtering ◽  
Least Squares Method ◽  
Optimal Algorithm ◽  
Heart Diseases ◽  
Cardiac Activity ◽  
Motion Artifacts ◽  
The Body ◽  
Advantages And Disadvantages

Постоянно растущий интерес к разработке новых неинвазивных и безманжетных методов измерения параметров сердечной деятельности, использование которых давало бы возможность непрерывного и удаленного контроля сердечно-сосудистой системы, обуславливает актуальность данной работы. В многочисленных публикациях продолжаются обсуждения преимуществ и недостатков различных методов ранней диагностики сердечно-сосудистых заболеваний. Однако артефакты движения являются сильной помехой, мешающей точной оценке показателей функционирования сердечно-сосудистой системы. Одним из перспективных методов контроля является метод оценки физиологических параметров с использованием фотоплетизмографии. Данная статья посвящена разработке устройства для фотоплетизмографических исследований и алгоритмических методов обработки регистрируемых сигналов для обеспечения мониторинга сердечного ритма с заданной точностью. В работе используются технологии цифровой адаптивной фильтрации полученных сигналов для мониторинга сердечного ритма в условиях внешних механических и электрических помеховых воздействий, ухудшающих точностные характеристики системы, а также разработана архитектура системы и изготовлен макет устройства, который позволил провести измерения для определения оптимального алгоритма цифровой обработки сигналов. При использовании устройства применялись методы адаптивной фильтрации на основе фильтров Винера, фильтров на основе метода наименьших квадратов и Калмановской фильтрации. Разработанное устройство для фотоплетизмографических исследований обеспечило возможность мониторинга сердечного ритма с заданной точностью, контроля текущего состояния организма и может быть использовано в качестве средства диагностики заболеваний сердца The constantly growing interest in the development of new non-invasive and cuff-free methods for measuring the parameters of cardiac activity, the use of which would give the possibility of continuous and remote monitoring of the cardiovascular system, determines the relevance of this work. Numerous publications continue to discuss the advantages and disadvantages of various methods of early diagnosis of cardiovascular disease. However, motion artifacts are a strong hindrance to the accurate assessment of the performance of the cardiovascular system. One of the promising control methods is the method for assessing physiological parameters using photoplethysmography. This article is devoted to the development of a device for photoplethysmographic studies and algorithmic methods for processing recorded signals to ensure monitoring of the heart rate with a given accuracy. The work uses technologies of digital adaptive filtering of the received signals to monitor the heart rate in conditions of external mechanical and electrical interference, which worsen the accuracy characteristics of the system, as well as the architecture of the system and a prototype of the device, which made it possible to carry out measurements to determine the optimal algorithm for digital signal processing. When using the device, the methods of adaptive filtering based on Wiener filters, filters based on the least squares method and Kalman filtering were used. The developed device for photoplethysmographic studies provided the ability to monitor the heart rate with a given accuracy, control the current state of the body and can be used as a means of diagnosing heart diseases

scholarly journals Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4298
Author(s):  
Alessandra Galli ◽  
Elisabetta Peri ◽  
Yijing Zhang ◽  
Rik Vullings ◽  
Myrthe van der Ven ◽  
...  
Keyword(s):  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Processing System ◽  
Motion Artifacts ◽  
Accurate Estimation ◽  
Fetal Heart Rate Monitoring ◽  
Dry Electrodes ◽  
Textile Electrodes ◽  
Dry Electrode

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.

scholarly journals Implantable computer-controlled adaptive multielectrode positioning system

2018 ◽  
Vol 119 (4) ◽  
pp. 1471-1484 ◽  
Author(s):  
E. Ferrea ◽  
L. Suriya-Arunroj ◽  
D. Hoehl ◽  
U. Thomas ◽  
A. Gail
Keyword(s):  
Cerebral Cortex ◽  
Large Scale ◽  
Nonhuman Primates ◽  
Tissue Response ◽  
High Signal ◽  
Signal Quality ◽  
Positioning System ◽  
Multielectrode Arrays ◽  
User Friendliness ◽  
Computer Controlled

Acute neuronal recordings performed with metal microelectrodes in nonhuman primates allow investigating the neural substrate of complex cognitive behaviors. Yet the daily reinsertion and positioning of the electrodes prevents recording from many neurons simultaneously, limiting the suitability of these types of recordings for brain-computer interface applications or for large-scale population statistical methods on a trial-by-trial basis. In contrast, chronically implanted multielectrode arrays offer the opportunity to record from many neurons simultaneously, but immovable electrodes prevent optimization of the signal during and after implantation and cause the tissue response to progressively impair the transduced signal quality, thereby limiting the number of different neurons that can be recorded over the lifetime of the implant. Semichronically implanted matrices of electrodes, instead, allow individually movable electrodes in depth and achieve higher channel count compared with acute methods, hence partially overcoming these limitations. Existing semichronic systems with higher channel count lack computerized control of electrode movements, leading to limited user-friendliness and uncertainty in depth positioning. Here we demonstrate a chronically implantable adaptive multielectrode positioning system with detachable drive for computerized depth adjustment of individual electrodes over several millimeters. This semichronic 16-channel system is designed to optimize the simultaneous yield of units in an extended period following implantation since the electrodes can be independently depth adjusted with minimal effort and their signal quality continuously assessed. Importantly, the electrode array is designed to remain within a chronic recording chamber for a prolonged time or can be used for acute recordings with high signal-to-noise ratio in the cerebral cortex of nonhuman primates. NEW & NOTEWORTHY We present a 16-channel motorized, semichronic multielectrode array with individually depth-adjustable electrodes to record in the cerebral cortex of nonhuman primates. Compared with fixed-geometry arrays, this system allows repeated reestablishing of single neuron isolation. Compared with manually adjustable arrays it benefits from computer-controlled positioning. Compared with motorized semichronic systems it allows higher channel counts due to a robotic single actuator approach. Overall the system is designed to optimize the simultaneous yield of units over the course of implantation.

Seizure detection using heart rate variability: A prospective validation study

Epilepsia ◽  
2020 ◽  
Vol 61 (S1) ◽  
Author(s):  
Jesper Jeppesen ◽  
Anders Fuglsang‐Frederiksen ◽  
Peter Johansen ◽  
Jakob Christensen ◽  
Stephan Wüstenhagen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Validation Study ◽  
Seizure Detection

scholarly journals A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 385 ◽  
Author(s):  
Yalan Ye ◽  
Wenwen He ◽  
Yunfei Cheng ◽  
Wenxia Huang ◽  
Zhilin Zhang
Keyword(s):  
Heart Rate ◽  
Random Forest ◽  
Motion Artifacts ◽  
Heart Rate Monitoring

Five New Things: Seizure Detection Devices

2021 ◽  
pp. 10.1212/CPJ.0000000000001044
Author(s):  
Alexandra Carrick Atwood ◽  
Cornelia Natasha Drees
Keyword(s):  
Heart Rate ◽  
Motion Detection ◽  
Electrodermal Activity ◽  
False Positive Rate ◽  
Seizure Detection ◽  
Mechanisms Of Action ◽  
Unexpected Death ◽  
Positive Rate ◽  
Convulsive Seizures ◽  
Electroencephalogram Eeg

ABSTRACT:Purpose: The purpose of this paper is to review seizure detection devices, their mechanisms of action, efficacy and reflecting upon potential improvements for future devices.Recent Findings: There are five main categories of seizure detection devices ([email protected]), these include electroencephalogram (EEG), heart rate detection (HR), electrodermal activity (EDA), motion detection and electromyography (EMG). These devices can be used in combination or in isolation to detect seizures. These devices are high in their sensitivity for convulsive seizures, but low in specificity because of a tendency to detect artifact. Overall, they perform poorly identifying non-convulsive seizures.Summary: Seizure detection devices are currently most useful in detecting convulsive seizures and thereby might help against sudden unexpected death in epilepsy (SUDEP), though they have a high false positive rate. These devices are much less adept at detecting more clinically subtle seizures.

Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE

2019 ◽  
Vol 29 (02) ◽  
pp. 2050024
Author(s):  
Mahesh B. Dembrani ◽  
K. B. Khanchandani ◽  
Anita Zurani
Keyword(s):  
Signal Analysis ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Digital Processor ◽  
Ica Algorithm

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

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