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2022 ◽  
Author(s):  
Bruce R Hopenfeld

Background: Obtaining reliable rate heart estimates from waist based electrocardiograms (ECGs) poses a very challenging problem due to the presence of extreme motion artifacts. The literature reveals few, if any, attempts to apply motion artifact cancellation methods to waist based ECGs. This paper describes a new methodology for ameliorating the effects of motion artifacts in ECGs by specifically targeting ECG peaks for elimination that are determined to be correlated with accelerometer peaks. This peak space cancellation was applied to real world waist based ECGs. Algorithm Summary: The methodology includes successive applications of a previously described pattern-based heart beat detection scheme (Temporal Pattern Search, or TEPS) that can also detect patterns in other types of peak sequences. In the first application, TEPS is applied to accelerometer signals recorded contemporaneously with ECG signals to identify high-quality accelerometer peak sequences (SA) indicative of quasi-periodic motion likely to impair identification of peaks in a corresponding ECG signal. The process then performs ECG peak detection and locates the closest in time ECG peak to each peak in an SA. The differences in time between ECG and SA peaks are clustered. If the number of elements in a cluster of peaks in an SA exceeds a threshold, the ECG peaks in that cluster are removed from further processing. After this peak removal process, further QRS detection proceeds according to TEPS. Experiment: The above procedure was applied to data from real world experiments involving four sessions of walking and jogging on a dirt road for approximately 20-25 minutes. A compression shirt with textile electrodes served as the ground truth recording. A textile electrode based chest strap was worn around the waist to generate a single channel signal upon which to test peak space cancellation/TEPS. Results: Both walking and jogging heart rates were generally well tracked. In the four recordings, the percentage of 5 second segments within 10 beats/minute of reference was 96%, 99%, 92% and 96%. The percentage of segments within 5 beats/minute of reference was 86%, 90%, 82% and 78%. There was very good agreement between the RR intervals associated with the reference and waist recordings. For acceptable quality segments, the root mean square sum of successive RR interval differences (RMSSD) was calculated for both the reference and waist recordings. Next, the difference between waist and reference RMSSDs was calculated (∆RMSSD). The mean ∆RMSSD (over acceptable segments) was 4.6 m, 5.2 ms, 5.2 ms and 6.6 ms for the four recordings. Conclusion: Given that only one waist ECG channel was available, and that the strap used for the waist recording was not tailored for that purpose, the proposed methodology shows promise for waist based sinus rhythm QRS detection.


Author(s):  
Roberta Pecoraro ◽  
Santi Concetto Pavone ◽  
Elena Maria Scalisi ◽  
Sara Ignoto ◽  
Carmen Sica ◽  
...  

5G technology is evolving to satisfy several service requirements favoring high data-rate connections and lower latency times than current ones (< 1ms). 5G systems use different frequency bands of the radio wave spectrum, taking advantage of higher frequencies than previous mobile radio generations. In order to guarantee a capillary coverage of the territory for high reliability applications, it will be necessary to install a large number of repeaters because higher frequencies waves have a lower capacity to propagate in free space. Following the introduction of this new technology, there has been a growing concern about possible harmful effects on human health. The aim of this study is investigating possible short term effects induced by 5G-millimeter waves on embryonic development of Danio rerio. We have exposed fertilized eggs to 27 GHz frequency, 9.7 mW/cm2 incident power density, 23 dbm and have measured several endpoints every 24 hours. The exposure to electromagnetic fields at 27 GHz (5G) caused no significant impacts on mortality nor on morphology because the exposed larvae showed a normal detachment of the tail, presence of heart-beat and well-organised somites. A weak positivity on exposed larvae has been highlighted by immunohistochemical analysis.


Author(s):  
U. Sravan

Abstract: An IoT based health monitoring system records the patient’s heart beat, body temperature, oxygen levels of blood etc. It can also be used to inform the timing of medication and provides live monitoring of health condition of patient to the doctor available in his chamber. It also sends an SMS alert whenever the health parameter readings go beyond critical values. Keywords: Heart beat, Temperature, Oxygen Levels, Medication timing, SMS alert.


2021 ◽  
Vol 38 (6) ◽  
pp. 1737-1745
Author(s):  
Amine Ben Slama ◽  
Hanene Sahli ◽  
Ramzi Maalmi ◽  
Hedi Trabelsi

In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmia datasets. By the use of this CNN model, we planned a complete structure of the classification architecture after a pre-processing stage counting the denoising and QRS complex signals detection procedure. The chief benefit of the new proposed methodology is that the automatically training the QRS complexes without requiring all original extracted ECG signals. The proposed model was trained on the increased ECG database and separated into five classes. Experimental results display that the established CNN method has improved performance when compared to the state-of-the-art studies.


Author(s):  
Y Wu ◽  
T Miwa ◽  
M Uchida

While simulator based maritime training is widely implemented under international maritime organization (IMO) convention and model courses, troublesome issues such as objective evaluation of training effectiveness remain unsolved. Physiological computing system (PhyCS) refers to an innovative bidirectional human computer interaction which is achieved by monitoring, analysing, and responding to operators’ psychophysiological activities in real-time. With the development of wearable devices, it becomes promising to apply PhyCS, which was considered as a laboratory technology, in real-world scenarios. In our experience utilizing view tracker, portable heart beat sensor, electroencephalogram device, and web-cameras in simulator based maritime training, PhyCS shows potential for advanced applications in operator performance assessment, usability tests, and adaptive training. However, ambulatory working environment, body movement artefact, and model verification are intricate obstacles that constrain its applications in the real world. By examining the advantages and obstacles, this paper aims to develop guidelines to apply PhyCS in the real-world.


2021 ◽  
Author(s):  
Florent El Grabli ◽  
François Quesque ◽  
Céline Borg ◽  
Michael Witthöft ◽  
George A Michael ◽  
...  

Aim: Lower interoceptive abilities are a characteristic of chronic pain conditions. Social support plays an important role in chronic low back pain (cLBP) but social cognitive skills have rarely been investigated. This study aimed to characterize interoceptive and social cognitive abilities in cLBP and to study the relationship between both domains that have been brought closer together by brain predictive coding models. Materials & methods: Twenty-eight patients with cLBP and 74 matched controls were included. Interoceptive accuracy (Heart Beat Perception Task), sensibility/awareness (Multidimensional Assessment of Interoceptive Awareness) and mental-states inference abilities (Mini-Social Cognition and Emotional Assessment) were assessed. Results: cLBP Patients had lower interoceptive accuracy and mentalizing performance. Conclusion: Less efficient interoceptive accuracy and mentalizing abilities were found in cLBP patients without correlation between these performances.


Author(s):  
Ana Brad ◽  
Maria Brad

Abstract This paper presents a "smart" clothing product implemented as a jacket that contains sensors, a processing unit for display and interaction. The system has the ability to remotely read the data provided by the sensors, ensuring the monitoring of several parameters of the wearer. The following characteristics have been considered: body temperature and humidity, atmospheric temperature, pressure and altitude, the heart beat and number of steps converted into the number of calories consumed and traveled distance. The data is acquired and processed by an Arduino AT Mega 2560, via the I2C bus, digital ports and analog to digital converters, depending on the type of sensors. The processed information is printed on a 128x64 pixel display. To be able to view more pages of information, one can interact with the 4-key keyboard that has been connected to the digital input ports or through a proximity sensor, which will function as a gesture sensor. The processed information can also be accessed from a web server, built on the ESP8266 Wi-Fi module, connected to Arduino's TX/RX lines. A mobile phone or another device can connect to the Access Point and open a web page which displays the values of all sensors, as well as other information. The embedded system was inserted on a jacket and wired according to the sensors and modules usage.


2021 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Andrea Bizzego ◽  
Giulio Gabrieli ◽  
Michelle Jin Yee Neoh ◽  
Gianluca Esposito

Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.


2021 ◽  
Vol 8 (6) ◽  
pp. 1255
Author(s):  
Asih Setiarini ◽  
Mahatma Widya Laksana ◽  
Basuki Winarno

<p class="Abstract">Lari merupakan olahraga yang efektif untuk membakar kalori. Namun, olahraga ini mempunyai dampak negatif bagi pelari yang mampu memicu serangan jantung sehingga dibutuhkan alat kesehatan untuk mendeteksi frekuensi denyut nadi saat berlari. Tujuan penelitian ini merancang sistem monitoring frekuensi denyut nadi secara real time pada pelari dengan menggunakan easily plugin pulse sensor berbasis photoplethysmographic. Sensor tersebut terdiri atas transmitter dan receiver infrared yang dipasang pada ujung jari tengah yang mana melalui jaringan kulit mampu mendeteksi volume darah. Fitur buzzer digunakan sebagai alarm jika denyut jantung mencapai 170 Beat Per Minute (BPM). Alat ini juga dilengkapi dengan aplikasi Android yang memudahkan pihak lain memonitoring keadaan denyut nadi pelari. Bluetooth HC-05 sebagai modul komunikasi data antara Arduino dan Android. Alat yang dirancang memiliki error maksimum sebesar 0,73% berdasarkan data percobaan dari 5 partisipan. Berdasarkan hasil pengujian, sistem monitoring frekuensi denyut nadi secara real time mampu mendeteksi serangan jantung saat berlari dan adanya fitur data logger digunakan untuk rekap medis keadaan frekuensi denyut jantung saat berlari tanpa menggunakan aplikasi smartphone Android.</p><p class="Abstract"> </p><p class="Abstract"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Running is the most popular workout around the world, because the most accessible, the cheapest and organized sport. However, running is dangerous in people suffering from heart disease. Hence, the medical device to detect heart failure for runners is required. In this paper, a monitoring system and data logger for detecting heart pulse by using easily plugin pulse sensor heart beat and its implementation for runners is newly proposed. The proposed method of sensor to detect the heart beat by using Photoplesthymograph principle. The sensor consists of transmitter and receiver infrared through to skin tissue to detect the blood volume. Different to previous work, the proposed device can be real time to monitor runners while running and have alarm when their heart beat reach 170 BPM. This device is also equipped with an Android application that facilitate other parties to monitor the runner’s heart beat. By using OMRON HEM-7203 as comparison devices, the rate error of measurement result is 0,086% within 5 participans. The proposed device is suitable for heart pulse monitoring system for runners in real time to reduce the heart attack while running.</em><em> </em></p><p class="Abstract"><em><strong><br /></strong></em></p>


Author(s):  
F. T. Oyediji ◽  
A. O. Aluko ◽  
A. O. Adetunmbi

Over the years, the decline of Nigeria’s health-care infrastructure has become alarming. The 2018 annual report of WHO shows that 75% human cardiovascular disease resulted from High Blood pressure. Immediate technical action is needed to alleviate the severity to the barest minimum. This research work presents a designs and implementation of microcontroller based Heart Beat Monitoring System for High Blood Pressure Patients. The developed system consists of three sections which include; Input section consisting of Heart beat sensor that senses and converts the blood pulse from a fingertip into an electrical signal. The sensor thereafter sends the signal into microcontroller, which is the control section that acts and communicates the result through LCD and output section. The displayed results show the beat rate in unit of beat per minute (BPM). The developed system was evaluated and demonstrated with two other standard devices namely: Pulse Oximeter and Digital Arm Cuff using a one-way analysis of variance (ANOVA) to determine its level of significance. The P Value of 0.519049 was found significant at 0.05 level of significance. Additionally, the results indicate that there is no significant difference among the three devices. It was concluded the designed device is more cost effective, user friendly and easily assembled due availability of needed materials in contrast with the other standard devices.


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