diff --git a/logic/biometrics.py b/logic/biometrics.py
index c101696..edeb25e 100644
--- a/logic/biometrics.py
+++ b/logic/biometrics.py
@@ -1,8 +1,8 @@
 from logic.base_logic import OptionalBaseLogic
 
 from brainflow.board_shim import BoardShim, BrainFlowPresets
-from brainflow.data_filter import DataFilter, AggOperations, NoiseTypes, FilterTypes, DetrendOperations, WindowOperations
-from scipy.signal import find_peaks
+from brainflow.data_filter import DataFilter, WaveletTypes
+from scipy.signal import find_peaks, butter, filtfilt
 
 import numpy as np
 import utils
@@ -14,7 +14,7 @@ class Biometrics(OptionalBaseLogic):
     RESP_FREQ = "BreathsPerSecond"
     RESP_BPM = "BreathsPerMinute"
 
-    def __init__(self, board, supported=True, fft_size=1024, ema_decay=0.025):
+    def __init__(self, board, supported=True, window_seconds=10, ema_decay=0.025):
         super().__init__(board, supported)
 
         if supported:
@@ -25,46 +25,46 @@ def __init__(self, board, supported=True, fft_size=1024, ema_decay=0.025):
             self.ppg_sampling_rate = BoardShim.get_sampling_rate(
                 board_id, BrainFlowPresets.ANCILLARY_PRESET)
 
-            self.window_seconds = int(fft_size / self.ppg_sampling_rate) + 1
+            self.window_seconds = window_seconds
             self.max_sample_size = self.ppg_sampling_rate * self.window_seconds
-            self.fft_size = fft_size
+
+            # heart rate filter params
+            lowcut = 30 / 60
+            highcut = 150 / 60
+            order = 2
+            b, a = butter(order, (lowcut, highcut), btype="bandpass", fs=self.ppg_sampling_rate)
+            self.hr_filter = lambda data: filtfilt(b, a, data)
+            self.min_distance = self.ppg_sampling_rate / highcut
 
             # ema smoothing variables
             self.current_values = None
             self.ema_decay = ema_decay
 
-    def estimate_heart_rate(self, hr_ir, hr_red, ppg_ambient):
+    def estimate_heart_rate(self, ppg_ir, ppg_red, ppg_ambient):
         # do not modify data
-        hr_ir, hr_red, hr_ambient = np.copy(hr_ir), np.copy(hr_red), np.copy(ppg_ambient)
-
-        # Possible min and max heart rate in hz
-        lowcut = 0.5
-        highcut = 4.25
-        order = 4
+        ppg_ir, ppg_red, ppg_ambient = np.copy(ppg_ir), np.copy(ppg_red), np.copy(ppg_ambient)
 
         # remove ambient light
-        hr_ir = np.clip(hr_ir - hr_ambient, 0, None)
-        hr_red = np.clip(hr_red - hr_ambient, 0, None)
-
-        # detrend and filter down to possible heart rates
-        DataFilter.detrend(hr_red, DetrendOperations.LINEAR)
-        DataFilter.detrend(hr_ir, DetrendOperations.LINEAR)
-        DataFilter.perform_bandpass(hr_red, self.ppg_sampling_rate, lowcut, highcut, order, FilterTypes.BUTTERWORTH, 0)
-        DataFilter.perform_bandpass(hr_ir, self.ppg_sampling_rate, lowcut, highcut, order, FilterTypes.BUTTERWORTH, 0)
-        
+        ppg_ir -= ppg_ambient
+        ppg_red -= ppg_ambient
+
+        # Denoise and Filter to possible heart rates
+        DataFilter.perform_wavelet_denoising(ppg_ir, WaveletTypes.DB4, 4)
+        DataFilter.perform_wavelet_denoising(ppg_red, WaveletTypes.DB4, 4)
+        ppg_ir = self.hr_filter(ppg_ir)
+        ppg_red = self.hr_filter(ppg_red)
+
         # find peaks in signal
-        red_peaks, _ = find_peaks(hr_red, distance=self.ppg_sampling_rate/2)
-        ir_peaks, _ = find_peaks(hr_ir, distance=self.ppg_sampling_rate/2)
+        red_peaks, _ = find_peaks(ppg_red, distance=self.min_distance)
+        ir_peaks, _ = find_peaks(ppg_ir, distance=self.min_distance)
 
-        # get inter-peak intervals
-        red_ipis = np.diff(red_peaks) / self.ppg_sampling_rate
-        ir_ipis = np.diff(ir_peaks) / self.ppg_sampling_rate
-        ipis = np.concatenate((red_ipis, ir_ipis))
+        # get inter-peak sample intervals
+        sample_ipis = np.concatenate((np.diff(red_peaks), np.diff(ir_peaks)))
         
-        # get bpm from mean inter-peak interval
-        average_ipi = np.mean(ipis)
+        # get bpm from mean inter-peak sample interval
+        average_ipi = np.mean(sample_ipis) / self.ppg_sampling_rate
         heart_bpm = 60 / average_ipi
-
+        
         return heart_bpm
     
     def calculate_data_dict(self):
diff --git a/main.py b/main.py
index 7e0cd07..640b264 100644
--- a/main.py
+++ b/main.py
@@ -124,9 +124,7 @@ def BoardInit(args: argparse.Namespace) -> tuple[BoardShim, list[BaseLogic], int
 
     ### Logic Modules ###
     has_muse_ppg = master_board_id in (BoardIds.MUSE_2_BOARD, BoardIds.MUSE_S_BOARD)
-    
-    fft_size= 64 * 10 # TODO: Make this configurable
-    biometrics_logic = Biometrics(board, has_muse_ppg, fft_size=fft_size, ema_decay=ema_decay)
+    biometrics_logic = Biometrics(board, has_muse_ppg, ema_decay=ema_decay)
 
     logics = [
         Info(board, window_seconds=window_seconds),
@@ -146,9 +144,6 @@ def BoardInit(args: argparse.Namespace) -> tuple[BoardShim, list[BaseLogic], int
     if args.enable_action:
         logics.append(MLAction(board, ema_decay = ema_decay * args.action_ema_multiplier))
 
-    ### Adding one second to startup time for adaptive filters ###
-    startup_time += 1
-
     BoardShim.log_message(LogLevels.LEVEL_INFO.value, 'Intializing (wait {}s)'.format(startup_time))
     board.start_stream(streamer_params=args.streamer_params)
     time.sleep(startup_time)