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While there are plenty of methods to detect R peaks it appears that detecting S peaks is less discussed. Is there a recommended way of detecting S peaks?

Additionally, could you recommend an implementation in python?

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  • $\begingroup$ I know extremely little about this to provide any valuable insight, but doesn't the S wave always immediately follow the R wave (in the opposite direction)? Such that if you can detect the R wave you can use that information to more accurately locate the S wave? More of a question since I am not in the biomedical field. $\endgroup$ Mar 18 '20 at 2:28
  • $\begingroup$ It does and I also think that detecting the R wave gives much information towards detecting the S wave. My concern is that this is more complex than it seems. $\endgroup$ Mar 23 '20 at 21:19
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If you are using Python you could try this:

def S_point(signal, R_peaks):

    num_peak=R_peaks.shape[0]
    S_point_list=list()
    for index in range(num_peak):
        i=R_peaks[index]
        cnt=i
        if cnt+1>=signal.shape[0]:
            break
        while signal[cnt]>signal[cnt+1]:
            cnt+=1
            if cnt>=signal.shape[0]:
                break
        S_point_list.append(cnt)
    return np.asarray(S_point_list)

If you allready have the R-peaks then you can use the signal and the R-peaks to get the S_points with this function

S_points=S_point(signal,R_peaks)

And finaly plot it like this

plt.plot(signal)
plt.plot(R_peak,signal[R_peak], 'yo')
plt.plot(S_points,signal[S_points], 'ro')

S-point and R-peak

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  • $\begingroup$ Thank you for the detailed answer. So you're essentially looking for the nearest point to the right of the R-peak where the downward trend stops. What if this single switch point is a bug somehow and the actual trend continues downward? Is there a common practice for smoothing / checking a window instead of single values? $\endgroup$ Apr 21 '20 at 12:36

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