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The effect of horror sound on electro-cardiac activity is not yet sufficiently explored despite recommendable research on different genres of music and sound. Detecting the impact of these stimuli is a complex task. The current study intends to automatically detect the horror-sound induced cardiac changes using wavelet coefficient-based features extracted from the electrocardiograph signals and several machine learning models. The statistical comparison of these features in the two data groups suggests a significant change in different feature values after exposure to horror sound while watching a horror clip. The statistically discriminative features were employed as input to nine machine learning (ML) models, namely Naïve Bayes, Fast large margin, Logistic regression, generalized linear model, Decision tree, Random Forest, deep learning, gradient boosted tree, and Support vector machine. The performance of these ML models was compared using different performance measures, including accuracy, precision, F-measure, sensitivity, specificity, and area under the curve (AUC). The results suggest the gradient boosted tree is the best performing model in differentiating the horror sound-induced changes in the ECG signals.