Discrimination of Arrhythmia Using a Smartphone
01 Jun 2018-
TL;DR: The proposed, Smartphone based inventive method of detection of AF utilizes Photoplythosmography (PPG) signal obtained from the fingertip using the camera in a smartphone, which differentiates Normal sinus rhythm (NSR) from AF, Premature Ventricular contraction (PVC) and Premature Atrial Contractions (PAC).
Abstract: The disturbed electrical activity of the heart which leads to Atrial Fibrillation (AF) poses a threat to the health and a lot of people in the country are suffering because of this. Its timely detection and treatment can increase the longevity of patients with AF. The proposed, Smartphone based inventive method of detection of AF utilizes Photoplythosmography (PPG) signal obtained from the fingertip using the camera in a smartphone. It differentiates Normal sinus rhythm (NSR) from AF, Premature Ventricular contraction (PVC) and Premature Atrial Contractions (PAC), with the combination of statistical methods like Root mean square of successive differences (RMSSD), in combination with Shannon Entropy (ShE) and Turning point ratio (TPR) method. The reformulated TPR is used along with pulse rise and fall times to increase the accuracy of detection. The acquired results show that this method can detect NSR with sensitivity of 0.9155, and gives overall accuracy of classification of 92%
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TL;DR: Smartphone-based healthcare technologies as discussed in academic literature according to their functionalities are classified, and the disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.
Abstract: Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.
1,007 citations
"Discrimination of Arrhythmia Using ..." refers background in this paper
...Since, smartphones are used on a daily basis by almost everyone they meet the criteria [6]....
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TL;DR: It is shown that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor.
Abstract: We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.
449 citations
"Discrimination of Arrhythmia Using ..." refers background in this paper
...al is based on smartphone application which captures the fingertip video recording that gives a pulasatile signal similar to heart fluctuations[7],[8]....
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TL;DR: A method for the automatic detection of atrial fibrillation, an abnormal heart rhythm, based on the sequence of intervals between heartbeats, using standard density histograms of the RR and ΔRR intervals as templates.
Abstract: The paper describes a method for the automatic detection of atrial fibrillation, an abnormal heart rhythm, based on the sequence of intervals between heartbeats. The RR interval is the interbeat interval, and deltaRR is the difference between two successive RR intervals. Standard density histograms of the RR and deltaRR intervals were prepared as templates for atrial fibrillation detection. As the coefficients of variation of the RR and deltaRR intervals were approximately constant during atrial fibrillation, the coefficients of variation in the test data could be compared with the standard coefficients of variation (CV test). Further, the similarities between the density histograms of the test data and the standard density histograms were estimated using the Kolmogorov-Smirnov test. The CV test based on the RR intervals showed a sensitivity of 86.6% and a specificity of 84.3%. The CV test based on the deltaRR intervals showed that the sensitivity and the specificity are both approximately 84%. The Kolmogorov-Smirnov test based on the RR intervals did not improve on the result of the CV test. In contrast, the Kolmogorov-Smirnov test based on the ARR intervals showed a sensitivity of 94.4% and a specificity of 97.2%.
294 citations
"Discrimination of Arrhythmia Using ..." refers background in this paper
...The previous work done gives an observation that in detection of AF, some sort of rhythm irregularity which is due to premature atrial contractions (PAC) and Premature Ventricular contractions (PVC) gives a false detection of AF [4]....
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TL;DR: In a prospectively recruited cohort of 76 participants undergoing cardioversion for AF, it is found that a novel algorithm analyzing signals recorded using an iPhone 4S accurately distinguished pulse recordings during AF from sinus rhythm.
291 citations
"Discrimination of Arrhythmia Using ..." refers background in this paper
...al is based on smartphone application which captures the fingertip video recording that gives a pulasatile signal similar to heart fluctuations[7],[8]....
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TL;DR: It is hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens.
Abstract: Atrial fibrillation (AF) affects three to five million Americans and is associated with significant morbidity and mortality. Existing methods to diagnose this paroxysmal arrhythmia are cumbersome and/or expensive. We hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4S for AF detection, we first used two databases, the MIT-BIH AF and normal sinus rhythm (NSR) to derive discriminatory threshold values between two rhythms. Both databases include RR time series originating from 250 Hz sampled ECG recordings. We rescaled the RR time series to 30 Hz so that the RR time series resolution is 1/30 (s) which is equivalent to the resolution from an iPhone 4S. We investigated three statistical methods consisting of the root mean square of successive differences (RMSSD), the Shannon entropy (ShE) and the sample entropy (SampE), which have been proved to be useful tools for AF assessment. Using 64-beat segments from the MIT-BIH databases, we found the beat-to-beat accuracy value of 0.9405, 0.9300, and 0.9614 for RMSSD, ShE, and SampE, respectively. Using an iPhone 4S, we collected 2-min pulsatile time series from 25 prospectively recruited subjects with AF pre- and postelectrical cardioversion. Using derived threshold values of RMSSD, ShE and SampE from the MIT-BIH databases, we found the beat-to-beat accuracy of 0.9844, 0.8494, and 0.9522, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF in the data. Using this criterion, we achieved an accuracy of 100% for both the MIT-BIH AF and iPhone 4S databases.
236 citations
"Discrimination of Arrhythmia Using ..." refers methods in this paper
...RR difference (RMSSD) and Shannon entropy (ShE) which is also used previously in the case of heart rhythm analysis [9]....
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