Data standardization is a fundamental process in which binary or multi- classification systems incorporate it as a sub-system in the classification- based question. Standardization can be called a mapping function moving from one space to another. Depending on the quality of the data, various types of normalization methods have been suggested. Research is underway recently on whether this approach is actually necessary. In this article, the various standardization methods efficiency is measured for the purpose of categorizing signal-based emotion with EEG. Binary classifier based on Naïve Bayes Classifier to classify the emotions. Only various kernel functions are considered for Naïve Bayes Classifier. While the experimental results may not show a substantial difference in performance between varrious types of normalization, the process of normalization generally improves emotion recognition classification efficiency.

The utilization of heart Electrocardiograms (ECGs) is to measure irregular heart rate and regularity and detection of an arrhythmia. Various ways are submitted and utilized for cardiogram feature extraction with a reasonable percentage of right detection. Although the problem stays open, especially with respect to superior detection accuracy in ECGs. In nature, The ECG signals are very sensitive signals, having voltage-level as low as 0.5-5 mv and frequency-elements fall into the range of 0.05-100Hz and the largest amount of the information received in the range of 0.05-45Hz. The recorded ECG signal includes various kinds of noises such as baseline wander, channel noise which becomes very critical to eliminating for the best clinical finding which assists in the patient. The utilization of the discrete wavelet transform (DWT) as wavelet transforms can be utilized to be a two-dimensional time scale process technique for feature extraction and classification task, therefore it’s appropriate for the non- stationary ECG signals (because of the sufficient range values and the shift in a timely) in LabVIEW. To implement the feature extraction and classification tasks, a separating wavelet transformation (consonant), and the wavelet transform can be two-dimensional time-scale practical technique was utilized. Hence, it is relevant for non-constant ECG signals (because of sufficient scale-values and transformation in timely) in LabVIEW. The flexibility, standard nature and simplicity to utilizing programming possible with LabVIEW, makes it less complex. The pro-posed algorithm is executed in two steps. First step, de-noises the signal from the cardiogram signal to get rid of the noise, then detects the pulse, our extracted parameters are heart rate, P wave amplitude, T wave amplitude, S value, Q value, R-value, P offset location, P onset location, T onset location, T offset location and the location of P, Q, R, S and T wave.