## 1 Introduction  
As the heart has a central role in human health, assessing its condition is a prime concern for healthcare professionals. The easiest way to do this non-invasively is by using a stethoscope to listen to the heartbeat on the chest. Sounds convey useful information about the functioning of the heart, allowing cardiologists to identify abnormalities. Recordings of heart sounds are referred to as phonocardiograms (PCGs). Generally, each beat consists of two sounds, S1 and S2, and various algorithms have been developed to analyze them. Another non-invasive method is measuring the electrical activity of the heart, resulting in an electrocardiogram (ECG). In this technique, electrodes are placed around the chest and other anatomically specified parts of the body. To obtain a complete view of ECG signals, medical standard ECG measurements use 12 leads. Other applications, such as wearable ECG straps, make do with fewer electrodes, commonly three. ECG signals allow the study of heart rate variability (HRV), which represents variation in inter-beat intervals. HRV analysis has a wide application range, not only in the medical field, but also in engineering, including affective computing. Cardiac activities cause corresponding mechanical movements and vibrations in skin. Usually, such movements are easy to sense above arteries in the neck, wrist, and chest. Pioneered by [PERSON], accelerometer-based chest vibration measurement is known as seismocardiography (SCG) [1]. Similar to PCG, a typical accelerometer signal includes S1 and S2 waves of each cardiac cycle. In this study, we use S1\({}^{\prime}\) and S2\({}^{\prime}\) to indicate waves in the SCG signal to avoid confusion with S1 and S2 in the PCG signal. Accelerometers provide simple and affordable instrumentation, but the user must deal with several challenges; for example, if subjects move during measurements, the accelerometer will capture these motions. Since the accelerometer is very sensitive, even small environmental vibration may induce noise [2]. Other challenges include respiration and coughing. Measuring adults in the clinical setting is not very difficult, because the patient can be instructed to stay still during the measurement. With infants and small children, however, this may prove more of a challenge. Another demanding task is measuring subjects during normal daily activities, such as walking and running, because motion artefact corrupts the signals. Considering several important fiducial points, such as aortic valve opening (AO) and closure (AC), SCG holds great potential for clinical applications, including the assessment of myocardial contractility. It provides a non-invasive estimate of hemodynamic parameters [3] and left ventricular performance in patients with cardiac disease via systolic time intervals [1] as well as a non-invasive monitoring of stroke volume and cardiac time intervals [4]. Moreover, SCG can be used to estimate pre-ejection period (PEP) [5]. Examples such as these provide evidence that the method offers great promise. In addition, it complements ECG by providing information about the mechanical behavior of the heart [6]. Figure 1 shows a typical SCG signal measured from the chest; corresponding signals can also be harvested with an accelerometer above the carotid artery. [PERSON] et al. labeled S1\({}^{\prime}\) and S2\({}^{\prime}\) as the systolic and diastolic phase [7], respectively, while AO and AC represent the peak of the systolic and diastolic phase. In Figure 1, we can clearly see that, although the S1\({}^{\prime}\) and S2\({}^{\prime}\) waves around the 13 th second are corrupted (see the shaded area in Figure 1), both waves are still visible. Analogous to S1 and S2, S1\({}^{\prime}\) tends to exhibit a larger amplitude than S2\({}^{\prime}\). Figure 1 also shows that a series of S1\({}^{\prime}\) followed by S2\({}^{\prime}\) builds the whole cardiac activity signal.