Long-term electrocardiogram (ECG) monitoring is essential for determining the arrhythmic burden, a critical clinical metric for diagnosing cardiovascular conditions. Traditionally, this burden is estimated using a Classify-and-Count (CC) approach, which labels individual heartbeats and aggregates results by counting predictions for each label. However, even state-of-the-art Deep Learning classifiers exhibit systematic biases that accumulate over long-term recordings, leading to significant diagnostic inaccuracies. This paper investigates the application of quantification techniques to estimate arrhythmia prevalence in long-term ECG signals from the MIT-BIH Arrhythmia Database. We compare several base classifiers paired with quantification algorithms against a high-performance Deep Learning baseline, LITETime, using the standard CC method. Our results demonstrate a quantification paradox: while the LITETime achieves superior beat-by-beat accuracy, simpler classifiers equipped with quantification adjustment layers, particularly the Expectation-Maximization Quantifier (EMQ), significantly reduce the Mean Absolute Error (MAE) in prevalence estimation. By correcting the systematic bias caused by Prior Probability Shifts, our framework provides a more reliable diagnostic tool for long-term monitoring and wearable cardiac devices.