In this paper, we propose a simple and low complexity pulse peak detection algorithm using cascaded recursive digital filters and a slope sum function (SSF) with an adaptive thresholding scheme. The algorithm first eliminates noises in the photoplethysmogram (PPG) using the cascaded lowpass and highpass digital filters. The filters have been designed with 3-dB cutoff frequencies of 11 Hz and 0.5 Hz, respectively. The filtered PPG signal is then transformed by the SSF. The SSF simplifies detecting the pulse peaks by enhancing the upslope of the PPG signal and suppressing the remainder. A threshold for identifying SSF peaks is updated using the median filter with an order of 5. This update makes the threshold adaptive to variations of SSF heights. The detected SSF peaks localize ranges for pulse peak detection. Finally, the pulse peak is identified by picking the local maxima within the range from an onset index of the SSF signal to the following zero index. In order to cope with over-detected and missed information, the proposed algorithm employs knowledge-based rules as postprocessing. The algorithm is tested on a database where PPG waveforms are collected from 127 subjects. The results are promising, suggesting that the method provides simpler but accurate pulse peak detection in real applications. 

This paper presents a robust method for pulse peak determination in a digital volume pulse (DVP) waveform with a wandering baseline. A proposed new method uses a modified morphological filter (MMF) to eliminate a wandering baseline signal of the DVP signal with minimum distortion and a slope sum function (SSF) with an adaptive thresholding scheme to detect pulse peaks from the baseline-removed DVP signal. Further in order to cope with over-detected and missed pulse peaks, knowledge based rules are applied as a postprocessor. The algorithm automatically adjusts detection parameters periodically to adapt to varying beat morphologies and fluctuations. Compared with conventional methods (highpass filtering, linear interpolation, cubic spline interpolation, and wavelet adaptive filtering), our method performs better in terms of the signal-to-error ratio, the computational burden (0.125 seconds for one minute of DVP signal analysis with the Intel Core 2 Quad processor @ 2.40 GHz PC), the true detection rate (97.32% with an acceptance level of 4 ms ) as well as the normalized error rate (0.18%). In addition, the proposed method can detect true positions of pulse peaks more accurately and becomes very useful for pulse transit time (PTT) and pulse rate variability (PRV) analyses.

This paper presents real-time signal processing algorithm for detection of onsets and peaks in Photoplethysmogram (PPG) waveform. Algorithm relies on the analysis of amplitude, slope and inter-beat intervals. The presented algorithm consists of four stages for characterizing PPG waveform. Preprocessing stage involves transformation of PPG since the original waveform is less impulsive and robust. In second stage, algorithm seeks for valid pulse detection in transformed signal complying with the amplitude threshold and inter-beat interval. On detection of valid pulses, algorithm then searches backward and forward in transformed signal for the detection of peaks and onsets. Further the detection parameters are made adaptive to comply with varying beat morphologies and fluctuations in baseline. All signal processing steps and decision logics are implemented with low computational complexity to make it applicable for compact ubiquitous health monitoring devices. On evaluation with our database, the algorithm achieved sensitivity of 96.89% and positive predictivity of 94.55% within an acceptance level of 12 ms.

The conventional P300-based character spelling BCI system consists of a character presentation paradigm and a classification system. In this paper, we propose modifications to both in order to increase the word typing speed and accuracy. In the paradigm part, we have modified the T9 (Text on Nine keys) interface which is similar to the keypad of mobile phones being used for text messaging. Then we have integrated a custom-built dictionary to give word suggestions to a user while typing. The user can select one out of the given suggestions to complete word typing. Our proposed paradigms significantly reduce the word typing time and make words typing more convenient by typing complete words with only few initial character spellings. In the classification part we have adopted a Random Forest (RF) classifier. The RF improves classification accuracy by combining multiple decision trees. We conducted experiments with five subjects using the proposed BCI system. Our results demonstrate that our system increases typing speed significantly: our proposed system took an average time of 1.83 minutes per word, while typing ten random words, whereas the conventional spelling required 3.35 minutes for the same words under the same conditions, decreasing the typing time by 45.37%.

No abstract