Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. However, these techniques are severely hampered by motion artifacts and are limited to heart rate detection. To address these shortcomings we present a new ECG wearable that is similar to the clinical approach for heart monitoring. Our device weightless and is ultra low power, extending the battery lifetime to over a month to make the device more appropriate for in-home health care applications. The device uses two electrodes activated by the user to measure the voltage across the wrists. The electrodes are made from a flexible ink and can be painted on to the device casing, making it adaptable for different shapes and users. Also show the result of heart rate of beats per minute (bpm) based on the RR interval (peaks) calculation. That means whether the heart function is normal or abnormal (Tachycardia, Bradycardia). between QRS complexes. QRS complex can be detected using for example algorithms from the field of artificial neural networks, genetic algorithms, wavelet transforms or filterbanks. Moreover the next way how to detect QRS complex is to use adaptive threshold. The direct methods for heart rate detection are ECG signal spectral analyse and Short-Term Autocorrelation method. Disadvantage of all these methods is their complicated implementation to microprocessor unit for real time heart rate frequency detection. Real time QRS detector and heart rate computing algorithm from resting 24 hours ECG signal for 8-bit microcontroller is described in. This algorithm is not designed for physical stress test with artefacts. The designed digital filters and heart rate frequency detection algorithms are very simple but robust. They can be used for ECG signal processing during physical stress test with muscle artefacts. They are suitable for easy implementation in C language to microprocessor unit in embedded device. Design of these methods has been very easy with Matlab tools and functions.