A processor implemented method for identifying cardiac abnormalities in multi-lead electrocardiograms (ECGs), the method comprising: receiving, via one or more hardware processors, a training data set comprising a plurality of ECG recordings with multilabel-multiclass cardiac abnormalities, wherein each of the plurality of ECGs are sampled at varying frequencies; down sampling, via the one or more hardware processors, the plurality of ECG recordings with a single frequency pre-identified statistically; pruning, via the one or more hardware processors, the down sampled plurality of ECG recordings to generate a pruned training dataset by eliminating one or more ECG recordings that fail to capture information related to at least one cardiac abnormality among a set of cardiac abnormalities identified as a set scoring classes; slicing, via the one or more hardware processors, each ECG recording in the pruned training data set to generate a windowed dataset of overlapping windows; applying, via the one or more hardware processors, a fulcrum-based data rebalancing technique on the windowed dataset to generate a rebalanced training dataset for the multiclass-multilabel cardiac abnormalities, the fulcrum-based data rebalancing technique comprising: deriving a fulcrum point ( N fulcrum) of the windowed dataset from class frequency of each window in the windowed dataset based on one or more class labels from among the set of scoring classes assigned to each window, wherein the fulcrum point is derived by dividing a total of class representations by a scoring class count, wherein one or more classes from among the set of scoring classes with class frequency above the fulcrum point are identified as majority classes, wherein classes with class frequency below the fulcrum point are identified as minority classes; and performing a random sampling on the windowed dataset that is identified under minority classes to reach the fulcrum point, wherein random sampling provides a representation at least equal to the fulcrum point to the minority classes, wherein the majority classes are excluded from the random sampling; and training, via the one or more hardware processors, a Hybrid Neural Network architecture based DL model using the rebalanced training dataset to classify input ECG signals. receiving, via one or more hardware processors, a training data set comprising a plurality of ECG recordings with multilabel-multiclass cardiac abnormalities, wherein each of the plurality of ECGs are sampled at varying frequencies; down sampling, via the one or more hardware processors, the plurality of ECG recordings with a single frequency pre-identified statistically; pruning, via the one or more hardware processors, the down sampled plurality of ECG recordings to generate a pruned training dataset by eliminating one or more ECG recordings that fail to capture information related to at least one cardiac abnormality among a set of cardiac abnormalities identified as a set scoring classes; slicing, via the one or more hardware processors, each ECG recording in the pruned training data set to generate a windowed dataset of overlapping windows; applying, via the one or more hardware processors, a fulcrum-based data rebalancing technique on the windowed dataset to generate a rebalanced training dataset for the multiclass-multilabel cardiac abnormalities, the fulcrum-based data rebalancing technique comprising: deriving a fulcrum point ( N fulcrum) of the windowed dataset from class frequency of each window in the windowed dataset based on one or more class labels from among the set of scoring classes assigned to each window, wherein the fulcrum point is derived by dividing a total of class representations by a scoring class count, wherein one or more classes from among the set of scoring classes with class frequency above the fulcrum point are identified as majority classes, wherein classes with class frequency below the fulcrum point are identified as minority classes; and performing a random sampling on the windowed dataset that is identified under minority classes to reach the fulcrum point, wherein random sampling provides a representation at least equal to the fulcrum point to the minority classes, wherein the majority classes are excluded from the random sampling; and deriving a fulcrum point ( N fulcrum) of the windowed dataset from class frequency of each window in the windowed dataset based on one or more class labels from among the set of scoring classes assigned to each window, wherein the fulcrum point is derived by dividing a total of class representations by a scoring class count, wherein one or more classes from among the set of scoring classes with class frequency above the fulcrum point are identified as majority classes, wherein classes with class frequency below the fulcrum point are identified as minority classes; and performing a random sampling on the windowed dataset that is identified under minority classes to reach the fulcrum point, wherein random sampling provides a representation at least equal to the fulcrum point to the minority classes, wherein the majority classes are excluded from the random sampling; and training, via the one or more hardware processors, a Hybrid Neural Network architecture based DL model using the rebalanced training dataset to classify input ECG signals.