Pulse-shaped signals require a tool that can accurately analyse and identify the peak characteristics in the spectrum. One recently developed tool available to analyse non-stationary pulse-shaped waveforms with a suitable peak reconstruction is semiclassical signal analysis (SCSA). SCSA is a signal representation method that decomposes a real positive signal y(t) into a set of squared eigenfunctions through the discrete spectrum of the Schr¨odinger operator. In this study, we apply SCSA in two directions. First, we propose a new signal denoising method based on the signal curvature. We use this technique to show that denoising the pulse-shaped signal by regularizing its curvature can yield better peak-preserving performance than traditional filters, such as moving average filter or wavelet. Second, we apply SCSA to biomedical signal analysis. The localization abilities of L2 normalized squared eigenfunctions are used in blood pressure (BP) estimation. Based on existing properties, the systolic and diastolic phases are separated into photoplethysmograms (PPGs), which are then used as features for BP estimation. In addition, the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database is used to test the application with more than 8000 subjects. Another application uses SCSA features to characterize EEG and MEG signals, leading to more accurate epileptic spike detection and diagnosis in epileptic patients. Both applications are validated using real datasets, which guarantees statistical reliability and motivates future work of this model in clinical applications and equipment designs.