Nevertheless, with the rapid increase of data, how to achieve better automatic ECG analysis has become the key point. Today, doctors and cardiologists diagnose cardiovascular disease mainly through electrocardiogram (ECG), precise ones of which are available.

IntroductionĬardiovascular disease has been the largest threat to human life for decades, and millions of people died due to delayed treatment.

The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis. A comparison is also made with two other algorithms, to prove our superiority. The detection rate is 99.41%, the sensitivity (Se) is 99.72%, and the specificity (Sp) is 99.69% on the MIT-BIH Arrhythmia database. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. An improved adaptive threshold algorithm for QRS detection is reported in this paper. However, the detection rate of the threshold algorithm still calls for improvement. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG) analysis algorithm plays an important role, whose first step is QRS detection. Cardiovascular disease is the first cause of death around the world.

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