Arrhythmias,　cardiac　rhythm　disorders,　demand　precise　diagnosis　for　effective　treatment　planning,　emphasizing　the　crucial　role　of　electrocardiogram　(ECG)　signal　interpretation.　While　deep　learning　excels　in　ECG　classification,　its　interpretability　remains　a　challenge.　Addressing　this　issue,　our　research　introduces　an　innovative　approach　employing　image-based　ECG　representations　and　cascading　deep　neural　networks　(CDNNs)　to　enhance　arrhythmia　detection　precision.　Initiating　with　the　transformation　of　12-lead　ECG　time-series　data,　particularly　focusing　on　Lead　II,　into　image-based　representations　using　relative　positioning　matrices　(RPM),　this　novel　conversion　captures　spatial　and　temporal　information　concurrently,　enriching　the　depth　of　our　data　analysis.　Subsequently,　CDNNs　play　a　pivotal　role　in　feature　extraction,　designed　to　automatically　extract　impactful　features　from　image-based　ECG　representations,　providing　profound　insights　into　cardiac　electrical　activity.　Extracted　features　serve　as　inputs　for　the　subsequent　deep　neural　network　for　classification.　Experimental　results　on　the　Shaoxing-Chapman　ECG　database,　encompassing　over　10,000　recordings　with　an　80/10/10　split,　80/20　split,　60/40　split,　and　10-fold　cross-validation,　showcase　a　remarkable　100%　accuracy　in　distinguishing　between　seven　and　four　different　arrhythmia　classes.　Incorporating　SHapley　Additive　exPlanations　(SHAP)　values,　our　methodology　offers　comprehensive　insights　into　the　model＇s　decision-making,　ensuring　interpretability.　Additionally,　Gradient-weighted　Class　Activation　Mapping　(Grad-CAM)　aids　feature　visualization,　elucidating　key　features　driving　predictions.　By　amalgamating　image-based　representation,　CDNNs,　and　interpretability　techniques,　our　research　not　only　attains　exceptional　classification　performance　but　also　boosts　model　transparency　and　trustworthiness.　These　findings　hold　promise　for　advancing　arrhythmia　diagnosis　and　enhancing　interpretability　in　deep　learning-based　medical　applications.