Biomedical

Vital Signs

Normal Ranges by Age

Blood Pressure Classification

Biological Signals

Electrocardiogram (ECG)

class ECGAnalysis:
    """ECG signal processing"""
    
    # Wave components
    WAVES = {
        "P": {"duration": "0.08-0.10s", "amplitude": "0.1-0.2 mV"},
        "QRS": {"duration": "0.06-0.10s", "amplitude": "1.0-2.0 mV"},
        "T": {"duration": "0.10-0.25s", "amplitude": "0.1-0.3 mV"},
        "U": {"duration": "0.16-0.24s", "amplitude": "0.05-0.2 mV"}
    }
    
    # Heart rate calculation
    def calculate_heart_rate(self, rr_interval_ms):
        """Calculate BPM from RR interval"""
        return 60000 / rr_interval_ms
    
    # Rhythm analysis
    def analyze_rhythm(self, rr_intervals):
        """
        HRV metrics
        - SDNN: Standard deviation of NN intervals
        - RMSSD: Root mean square of successive differences
        - pNN50: % of successive intervals > 50ms
        """
        import numpy as np
        
        # RMSSD
        successive_diffs = np.diff(rr_intervals)
        rmssd = np.sqrt(np.mean(successive_diffs ** 2))
        
        # pNN50
        nn50 = sum(1 for d in successive_diffs if abs(d) > 50)
        pnn50 = (nn50 / len(successive_diffs)) * 100 if len(successive_diffs) > 0 else 0
        
        return {"RMSSD": rmssd, "pNN50": pnn50}
    
    # QRS detection
    def detect_qrs(self, ecg_signal, fs):
        """
        Pan-Tompkins algorithm
        1. Bandpass filter (5-15 Hz)
        2. Differentiate
        3. Square
        4. Moving window integrate
        5. Adaptive threshold
        """
        pass

Electroencephalogram (EEG)

Electromyogram (EMG)

class EMGProcessing:
    """EMG signal analysis"""
    
    def rect_filter(self, emg_signal, fs):
        """
        Process EMG:
        1. Bandpass filter (20-500 Hz)
        2. Full-wave rectification
        3. Moving average (smoothing)
        """
        pass
    
    def calculate_mav(self, emg_segment):
        """Mean Absolute Value"""
        return sum(abs(x) for x in emg_segment) / len(emg_segment)
    
    def calculate_rms(self, emg_segment):
        """Root Mean Square"""
        import numpy as np
        return np.sqrt(sum(x**2 for x in emg_segment) / len(emg_segment))
    
    def calculate_fatigue(self, emg_segment1, emg_segment2):
        """
        Detect fatigue via frequency shift
        - Median frequency decreases with fatigue
        """
        import numpy as np
        
        freq1 = np.median(np.abs(np.fft.rfft(emg_segment1)))
        freq2 = np.median(np.abs(np.fft.rfft(emg_segment2)))
        
        return freq2 < freq1  # True if fatigue detected

Medical Devices

Device Classification (FDA)

Device Categories

Healthcare Data Standards

HL7 FHIR Resources

DICOM

class DICOMHandler:
    """DICOM file handling"""
    
    # Important tags
    IMPORTANT_TAGS = {
        "0010,0010": "Patient Name",
        "0010,0020": "Patient ID",
        "0008,0060": "Modality",
        "0020,000D": "Study Instance UID",
        "0008,1030": "Study Description",
        "0028,0010": "Rows (image height)",
        "0028,0011": "Columns (image width)",
        "0028,0100": "Bits Allocated",
        "0028,0101": "Bits Stored"
    }
    
    def load_dicom(self, filepath):
        """Load DICOM file"""
        import pydicom
        
        ds = pydicom.dcmread(filepath)
        
        return {
            "patient": ds.PatientName,
            "modality": ds.Modality,
            "rows": ds.Rows,
            "columns": ds.Columns,
            "pixel_data": ds.pixel_array
        }
    
    def window_level(self, image, window, level):
        """
        Apply window/level for viewing
        """
        import numpy as np
        
        lower = level - window / 2
        upper = level + window / 2
        
        return np.clip((image - lower) / (upper - lower), 0, 1)

Medical Terminology

Biomedical Instrumentation

Amplifier Requirements

Safety Standards

Common Errors to Avoid

1. Ignoring electrode placement — Standard 12-lead ECG positions critical
2. Not filtering properly — Wrong filters distort signals
3. Confusing correlation with causation — Signals show association
4. Ignoring artifact contamination — Motion, electrical noise
5. Using wrong device class — Regulatory implications
6. Not considering biocompatibility — Material-tissue interactions
7. Ignoring calibration — Regular calibration needed for accuracy
8. Confusing device standards — Different regions have different requirements