Advanced wavelet decomposition of 47,052 electrocardiograms uncovered 67 genetic loci associated with frequency-specific cardiac electrical patterns, including variants in key genes like SCN5A, TTN, and KCNQ1. Most strikingly, high-frequency signals (125-250 Hz) typically filtered out as 'noise' showed a 0.56 genetic correlation with heart failure risk. This challenges decades of clinical practice that discards these frequencies during ECG processing. The analysis identified heritable patterns across multiple frequency bands, with strongest genetic signals in mid-range frequencies (4-32 Hz) and heritability estimates reaching 0.26 for certain features. These findings suggest the standard clinical approach of reducing complex ECG waveforms to simple measurements like QRS duration and PR interval may be throwing away crucial cardiovascular risk information. The genetic architecture appears coordinated across different leads, indicating systematic rather than random frequency patterns. However, this preprint awaits peer review, and validation in diverse populations beyond White British participants will be essential. If confirmed, this could revolutionize ECG interpretation by incorporating previously ignored frequency domains into cardiovascular risk assessment, potentially improving early detection of heart failure and other cardiac conditions.