• 대한의용생체공학회:의공학회지
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• 제43권4호
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• pp.280-289
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• 2022

In this paper, we present a QRS-complex detection algorithm to calculate an accurate heartbeat and clearly recognize irregular rhythm from ECG signals. The conventional Pan-Tompkins algorithm brings false QRS detection in the derivative when QRS and noise signals have similar instant variation. The proposed algorithm uses amplitude differences in 7 adjacent samples to detect QRS-complex which has the highest amplitude variation. The calculated amplitude is cubed to dominate QRS-complex and the moving average method is applied to diminish the noise signal's amplitude. Finally, a decision rule with a threshold value is applied to detect accurate QRS-complex. The calculated signals with Pan-Tompkins and proposed algorithms were compared by signal-to-noise ratio to evaluate the noise reduction degree. QRS-complex detection performance was confirmed by sensitivity and the positive predictive value(PPV). Normal ECG, muscle noise ECG, PVC, and atrial fibrillation signals were achieved which were measured from an ECG simulator. The signal-to-noise ratio difference between Pan-Tompkins and the proposed algorithm were 8.1, 8.5, 9.6, and 4.7, respectively. All ratio of the proposed algorithm is higher than the Pan-Tompkins values. It indicates that the proposed algorithm is more robust to noise than the Pan-Tompkins algorithm. The Pan-Tompkins algorithm and the proposed algorithm showed similar sensitivity and PPV at most waveforms. However, with a noisy atrial fibrillation signal, the PPV for QRS-complex has different values, 42% for the Pan-Tompkins algorithm and 100% for the proposed algorithm. It means that the proposed algorithm has superiority for QRS-complex detection in a noisy environment.

• 센서학회지
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• 제28권1호
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.

• 반도체디스플레이기술학회지
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• 제11권3호
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• pp.57-61
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• 2012

In order to analyze the ECG, it is important to pinpoint the repeat interval of the ECG. In this paper, we present an efficient algorithm for finding the QRS complex on the ECG. We escaped from the conventional methods to go through multiple steps of preprocessing to make ECG easier to find the QRS complex. We selected the candidate peak that has the possibility of the QRS complex in original ECG, and used technique to find the QRS complex among the candidate peak. In this way, we could get similar efficiency to Pan & Tompkins Algorithm that is most representative QRS complex detection algorithm, just used fewer operations than Pan & Tompkins Algorithm.

• 대한의용생체공학회:의공학회지
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• 제43권6호
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• pp.399-408
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• 2022

Because arrhythmia occurs irregularly, it should be examined for at least 24 hours for accurate diagnosis. For this reason, this paper developed firmware software for arrhythmia detection and prevented consumption of temporal and human resources and enabled continuous management and early diagnosis. Prior to the experiment, the interval between the R peaks of the QRS Complex was calculated using the Pan-Tompkins algorithm. The developed firmware software designed and implemented an algorithm to detect arrhythmia such as tachycardia, bradycardia, ventricular tachycardia, persistent tachycardia, and non-persistent tachycardia, and a data transmission format to monitor the collected data based on BLE. As a result of the experiment, arrhythmia was found in real time according to the change in BPM as designed in this paper. And the data quality for BLE communication was verified by comparing the sensor's serial communication value with the Android application reception value. In the future, wearable devices for real-time arrhythmia detection will be lightweight and developed firmware software will be applied.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2012년도 추계학술대회
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• pp.246-248
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• 2012

심전도는 측정 기기에서 발생하는 전기적 잡음이나 근육에서 발생하는 근전도에 의한 잡음, 전극을 부착한 사람의 움직임에 의한 동잡음 등에 민감한 특성을 보인다. 또한 심장의 이상으로 인하여 왜곡이 심하게 발생하므로 심전도에서 의미 있는 구간을 검출하기 위해서는 이들을 보완하는 알고리즘이 필수적이라 할 수 있다. 논문에서는 심전도 분석의 보조를 위하여 입력된 심전도가 가지는 잡음과 왜곡을 제거하고 구간의 위치를 출력하는 시스템을 제안한다. 이를 위해 관련 알고리즘 중, 가장 널리 알려진 'Pan & tompkins algorithm'을 시스템에 이식하였고 알고리즘의 각 단계를 알아보기 쉽게 출력하는 인터페이스를 구성하였다. 시스템의 기능을 확인하기 위해 MIT/BIH 데이터베이스를 이용하였으며, 잡음과 왜곡이 심하여 육안으로 구간을 확인하기 힘든 심전도에서도 높은 구간검출 정확도를 확인할 수 있었다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 춘계학술발표대회
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• pp.887-889
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• 2016

본 연구는 전도성 섬유를 양말에 부착하여 발바닥에서 심전도(ECG, Electrocardiogram) 신호를 검출하였다. 발바닥에서 측정한 심전도 신호와 손목에서 측정한 심전도 신호에 Pan-Tompkins algorithm을 적용하였고 R-R interval을 검출하였다. 이후 발바닥과 손목에서 측정된 심전도의 유의성을 검출하기 위해 비모수 검정법인 Spearman검정을 사용하여 상관분석을 수행하였다. 상관분석 결과, 유의확률 p=0.00에서 correlation coefficient=0.901로 두 데이터는 강한 양의 선형 관계에 있는 것으로 나타났다.

• 대한의용생체공학회:의공학회지
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• 제35권3호
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• pp.42-49
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• 2014

We propose an individual identification method using a single-lead electrocardiogram signal. In this paper, lead I ECG is measured from subjects in various physical and psychological states. We performed a noise reduction for lead I signal as a preprocessing stage and this signal is used to acquire the representative beat waveform for individuals by utilizing the ensemble average. From the P-QRS-T waves, features are extracted to identify individuals, 19 using the duration and amplitude information, and 16 from the QRS complex acquired by applying Pan-Tompkins algorithm to the ensemble averaged waveform. To analyze the effect of each feature and to improve efficiency while maintaining the performance, Relief-F algorithm is used to select features from the 35 features extracted. Some or all of these 35 features were used in the support vector machine (SVM) learning and tests. The classification accuracy using the entire feature set was 98.34%. Experimental results show that it is possible to identify a person by features extracted from limb lead I signal only.