update rpeak detection

vital_sqi/common/rpeak_detection.py

@@ -315,6 +315,68 @@ def detect_peak_trough_adaptive_threshold(
 
         return np.array(peak_finalist), np.array(trough_finalist)
 
+    def get_ROI(self, s, adaptive_threshold, margin=0.1):
+        """
+        Identify regions of interest (ROIs) in the signal where peaks or troughs are likely to occur.
+
+        Parameters
+        ----------
+        s : array_like
+            Input signal.
+        adaptive_threshold : array_like
+            Adaptive threshold values for the signal.
+        margin : float, optional
+            Margin (fraction of the signal range) to include before and after the ROI (default is 0.1).
+
+        Returns
+        -------
+        tuple
+            Two lists: start_ROIs and end_ROIs, which contain the start and end indices of the ROIs.
+
+        Notes
+        -----
+        - ROIs are defined as contiguous regions where the signal exceeds the adaptive threshold.
+        - Margins can be added to widen the ROIs for more inclusive peak/trough detection.
+
+        Example
+        -------
+        >>> s = [0, 1, 3, 7, 5, 2, 0, 6, 9, 8, 4, 1, 0]
+        >>> adaptive_threshold = [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
+        >>> peak_detector = PeakDetection(fs=100)
+        >>> start_ROIs, end_ROIs = peak_detector.get_ROI(s, adaptive_threshold)
+        """
+        # Identify regions where the signal exceeds the adaptive threshold
+        above_threshold = s > adaptive_threshold
+
+        # Find the start and end indices of these regions
+        start_ROIs = []
+        end_ROIs = []
+
+        for i in range(1, len(above_threshold)):
+            # Transition from below threshold to above threshold: start of an ROI
+            if above_threshold[i] and not above_threshold[i - 1]:
+                start_ROIs.append(i)
+
+            # Transition from above threshold to below threshold: end of an ROI
+            if not above_threshold[i] and above_threshold[i - 1]:
+                end_ROIs.append(i - 1)
+
+        # Handle case where the signal ends above the threshold
+        if above_threshold[-1]:
+            end_ROIs.append(len(s) - 1)
+
+        # Apply margin to widen the ROIs
+        signal_length = len(s)
+        start_ROIs = [
+            max(0, int(start - margin * signal_length)) for start in start_ROIs
+        ]
+        end_ROIs = [
+            min(signal_length - 1, int(end + margin * signal_length))
+            for end in end_ROIs
+        ]
+
+        return start_ROIs, end_ROIs
+
     def detect_peak_trough_moving_average_threshold(self, s):
         """
         Detects peaks using a moving average threshold approach.