Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

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Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases During the Timed-Up and Go Test

10.20944/preprints202008.0159.v1 ◽

2020 ◽

Author(s):

Vasco Ponciano ◽

Ivan Miguel Pires ◽

Fernando Reinaldo Ribeiro ◽

María Vanessa Villasana ◽

Maria Canavarro Teixeira ◽

...

Keyword(s):

Heart Rate ◽

Experimental Study ◽

Heart Rate Variability ◽

Mobile Devices ◽

Brain Activity ◽

Health Conditions ◽

Qrs Complex ◽

Timed Up And Go ◽

Eeg Data ◽

Ecg Data

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual's heartbeat. Similarly, by using the EEG sensor one could analyze the individual's brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly's experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

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Machine Learning Techniques with ECG and EEG Data: An Exploratory Study

Computers ◽

10.3390/computers9030055 ◽

2020 ◽

Vol 9 (3) ◽

pp. 55

Author(s):

Vasco Ponciano ◽

Ivan Miguel Pires ◽

Fernando Reinaldo Ribeiro ◽

Nuno M. Garcia ◽

María Vanessa Villasana ◽

...

Keyword(s):

Heart Rate ◽

Exploratory Study ◽

Low Cost ◽

Machine Learning Techniques ◽

Automatic Identification ◽

Timed Up And Go ◽

Learning Techniques ◽

Eeg Data ◽

The Relationship ◽

Ecg Data

Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.

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50 Hz magnetic field affects heart rate variability – an experimental study

2018 EMF-Med 1st World Conference on Biomedical Applications of Electromagnetic Fields (EMF-Med) ◽

10.23919/emf-med.2018.8526072 ◽

2018 ◽

Author(s):

Tarmo Koppel ◽

Inese Vilcane ◽

Mikko Ahonen

Keyword(s):

Magnetic Field ◽

Heart Rate ◽

Experimental Study ◽

Heart Rate Variability

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Evaluation of the Human Thermal Comfort under Simulated Weightlessness: an Experimental Study Based on the Power Spectrum Analysis of the Heart Rate Variability

Microgravity Science and Technology ◽

10.1007/s12217-018-9669-7 ◽

2018 ◽

Vol 31 (1) ◽

pp. 73-83 ◽

Cited By ~ 2

Author(s):

Hui Zhu ◽

Hanqing Wang ◽

Duanru Li ◽

Zijian Xiao ◽

Hua Su ◽

...

Keyword(s):

Heart Rate ◽

Experimental Study ◽

Heart Rate Variability ◽

Power Spectrum ◽

Thermal Comfort ◽

Spectrum Analysis ◽

Power Spectrum Analysis ◽

Simulated Weightlessness ◽

Human Thermal Comfort

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A new analysis of heart rate variability in the assessment of fetal parasympathetic activity: An experimental study in a fetal sheep model

PLoS ONE ◽

10.1371/journal.pone.0180653 ◽

2017 ◽

Vol 12 (7) ◽

pp. e0180653 ◽

Cited By ~ 11

Author(s):

C. Garabedian ◽

C. Champion ◽

E. Servan-Schreiber ◽

L. Butruille ◽

E. Aubry ◽

...

Keyword(s):

Heart Rate ◽

Experimental Study ◽

Heart Rate Variability ◽

Parasympathetic Activity ◽

Sheep Model ◽

Fetal Sheep

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An Experimental Study of Step Test Index Combined With Heart Rate Variability in Estimating Maximum Oxygen Uptake in Women With Drug Use Disorder

Frontiers in Physiology ◽

10.3389/fphys.2020.00322 ◽

2020 ◽

Vol 11 ◽

Author(s):

Kun Wang ◽

Tingran Zhang ◽

Yiyi Ouyang ◽

Haonan Jiang ◽

Meichen Qu ◽

...

Keyword(s):

Heart Rate ◽

Experimental Study ◽

Heart Rate Variability ◽

Drug Use ◽

Oxygen Uptake ◽

Maximum Oxygen Uptake ◽

Step Test ◽

Drug Use Disorder

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ECG SIGNAL GENERATION AND HEART RATE VARIABILITY SIGNAL EXTRACTION: SIGNAL PROCESSING, FEATURES DETECTION, AND THEIR CORRELATION WITH CARDIAC DISEASES

Journal of Mechanics in Medicine and Biology ◽

10.1142/s021951941240012x ◽

2012 ◽

Vol 12 (04) ◽

pp. 1240012 ◽

Cited By ~ 4

Author(s):

GOUTHAM SWAPNA ◽

DHANJOO N. GHISTA ◽

ROSHAN JOY MARTIS ◽

ALVIN P. C. ANG ◽

SUBBHURAAM VINITHA SREE

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Sinus Bradycardia ◽

Premature Ventricular Contraction ◽

P Wave ◽

Ecg Signal ◽

Qrs Complex ◽

T Wave ◽

St Segment ◽

Cardiac Disorders

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.

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Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance

BMC Research Notes ◽

10.1186/s13104-020-05355-0 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

M. M. van Gilst ◽

B. M. Wulterkens ◽

P. Fonseca ◽

M. Radha ◽

M. Ross ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Direct Application ◽

Alternative Methods ◽

Sleep Staging ◽

Practical Application ◽

Sleep Monitoring ◽

Data Source ◽

Variability Data ◽

Ecg Data

Abstract Objective The maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived from ECG-data. The practical application of these algorithms is even more promising when alternative ways of obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating sleep staging algorithms directly on PPG-based data are limited. Results We applied an automatic sleep staging algorithm trained and validated on ECG-data directly on inter-beat intervals derived from a wrist-worn PPG sensor, in 389 polysomnographic recordings of patients with a variety of sleep disorders. While the algorithm reached moderate agreement with gold standard polysomnography, the performance was significantly lower when applied on PPG- versus ECG-derived heart rate variability data (kappa 0.56 versus 0.60, p < 0.001; accuracy 73.0% versus 75.9% p < 0.001). These results show that direct application of an algorithm on a different source of data may negatively affect performance. Algorithms need to be validated using each data source and re-training should be considered whenever possible.

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