The automatic detection of cardiac arrhythmias through remote monitoring is still a challenging task since electrocardiograms (ECGs) are easily contaminated by physiological artifacts and extal noises,and these morphological characteristics show significant variations for different patients.A fast patient-specific arrhythmia diagnosis classifier scheme is proposed,in which a wavelet adaptive threshold denoising is combined with quantum genetic algorithm (QAG) based on least squares twin support vector machine (LSTSVM).The wavelet adaptive threshold denoising is employed for noise reduction,and then morphological features combined with the timing interval features are extracted to evaluate the classifier.For each patient,an individual and fast classifier will be trained by common and patient-specific training data.Following the recommendations of the Association for the Advancements of Medical Instrumentation (AAMI),experimental results over the MITBIH arrhythmia benchmark database demonstrated that our proposed method achieved the average detection accuracy of 98.22％,99.65％ and 99.41％ for the abnormal,ventricular ectopic beats (VEBs) and supra-VEBs(SVEBs),respectively.Besides the detection accuracy,sensitivity and specificity,our proposed method consumes the less CPU running time compared with the other representative state of the art methods.It can be ported to Android based embedded system,henceforth suitable for a wearable device.