3. Introduction
Cardiac dysrhythmia, known as arrhythmia, is a medical condition
where the rhythm of the heart is irregular, faster or slower than
average.
The average healthy human adult has a heart rate of 60-70 heart beats
per minute
Three forms of arrhythmia
Tachycardia – When the heart rate exceeds 90 heart beats per minute
Bradycardia – When the heart rate is less than 60 heart beats per minute
Irregular – Inconsistent heart rhythm
4. Significance of Problem
Each year in the United States, around 500,000 deaths occur
from arrhythmia.
Arrhythmia In the atria results in inefficient flow of blood to the
rest of the body.
Can result in shortness of breath, blood clots and even a stroke.
However, there can be a 15-20% decrease in the number of
deaths if there is a correct and early diagnosis.
5. Objective
The objective of this research is to analyze Electrocardiogram
(ECG) signals to determine any onsets of arrhythmia.
The primary questions is whether the algorithm can accurately
detect sinus tachycardia and bradycardia, along with any
irregular heart rhythms.
6. Study Population
The data was acquired from physionet’s online database;47 different
ECG signals were obtained.
The study population had an age range from 23-89 years old, the
average age of the patient was 63.
In the study, there were 21 males and 26 females.
It is important that the people do not eat or drink anything prior to the
test as it could sway the results (ex caffeine).
The data contains four different types of ECG’s: regular, tachycardia,
bradycardia and those who have an irregular heartbeat.
7. Study Population
Each ECG downloaded contained an array of voltages (in mV).
A typical ECG lasts about (30-40seconds); the length of the
acquired test was chosen to be one minute.
The signals were sampled at 360Hz.
8. Methods
A modified Pan-Tompkins algorithm was used to analyze the
ECG signals.
The original algorithm works by passing the signal through a
low pass filter (to remove noise), a high pass filter (accentuate
QRS peaks) and a derivative filter. It is then squared, passed
through a moving average filter and then through a thresholding
technique to detect R-peaks
9. Methods (Modified Algorithm)
Bandpass – reduce noise and baseline drift
Derivative filter – identifies QRS complex
Squaring operation – increases frequencies
Moving Average – signal is smoothed to highlight the QRS
complex
Thresholding – Detects two types of peaks; the QRS complex
and T waves. Uses a search back technique to detect each R
peak
10. Detecting Irregularities
The algorithm will determine if a heartbeat is irregular
1. Calculates time period differences between each peak
2. Finds the difference of the two differences between peaks, and
compares to a tolerance level estimated to allow small number of
premature contractions
3. If the QRS difference is greater than the tolerance level, then the
program detects that segment as an irregularity.
4. The algorithm deems a ECG signal as irregular if it counts more
than 8 irregularities
30. Discussion
Out of the 47 patients, 43 had a correct heart rate calculated by
the algorithm (a 91.48% success rate).
Better than original algorithm (a 72.3% success rate)
Problem with original algorithm is that it filtered the signal so
much that some of the peaks were reduced below the threshold
which caused inaccurate calculation of heartbeats/minute.
It was important to make modifications so that the sampling and
cut-off frequencies kept the QRS peaks intact.
31. Future
The algorithm can be modified for future use to include
detection of life threatening heart rhythms (ventricular
fibrillation)
As a result there is no P wave, T wave and the QRS is
elongated and occurs rapidly without a refractory period.
The algorithm can be modified to detect such occurrences by
detecting absence of p waves. By detecting absence of p
waves and measuring if the BPM is extremely high over small
periods of time.
32. Conclusion
The algorithm was successful in the primary objective of
determining arrhythmic heart rhythm from the given ECG data.
The algorithm correctly identified sinus tachycardia and sinus
bradycardia, while had a 91.48% overall success rate of
identifying normal and arrhythmic heart rhythms.
33. References
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