In this paper, we present a new method based on deep learning for the detection of cardiac arrhythmias. The proposed method is based on three fundamental components: an inception-ResNet structure, bidirectional LSTM layers and a multi-scale signal analysis technique. To improve processing of the MIT-BIH arrhythmia dataset, we have developed a three-stage CBD filter that successively incorporates Chebyshev, Butterworth and Daubechies filters. This filter is designed meticulously to denoise the ECG signal while preserving its relevant features. By combining this filter with our deep learning model and multi-scale approach, we have significantly improved the quality of the ECG signal. Such integration not only enhances the accuracy and reliability of arrhythmia detection, but also contributes to the identification of complex patterns in various temporal and spatial dimensions. In terms of performance, our model is remarkably robust, with 98.94% accuracy and 97.20% precision.