NeuralBlitz What I do
- Design medical devices and diagnostic equipment
- Process physiological signals (ECG, EEG, EMG)
- Analyze medical imaging data (CT, MRI, ultrasound)
- Model biological systems and biomechanics
- Design prosthetics and orthotics
- Implement FDA regulatory compliance
- Create clinical decision support systems
- Design implantable medical devices
- Implement medical data security (HIPAA)
- Validate medical device software
When to use me
When designing medical devices, processing physiological signals, implementing healthcare software, or ensuring regulatory compliance for medical products.
Core Concepts
- Physiological signal acquisition and processing
- Medical imaging modalities and analysis
- Biomechanics and rehabilitation engineering
- Medical device design and validation
- FDA and international regulatory requirements
- HIPAA data security and privacy
- Clinical trials and human factors engineering
- Biosensors and instrumentation
- Neural interfaces and brain-computer interfaces
- Biomaterials and tissue engineering
Code Examples
ECG Signal Processing
import numpy as np
from scipy import signal
from scipy.ndimage import uniform_filter1d
from dataclasses import dataclass
@dataclass
class ECGSignal:
data: np.ndarray
sampling_rate: float
patient_id: str
def detect_r_peaks(
ecg: ECGSignal,
threshold: float = 0.5,
min_distance: int = None
) -> np.ndarray:
"""Detect R-peaks in ECG signal using Pan-Tompkins algorithm."""
fs = ecg.sampling_rate
# Bandpass filter (5-15 Hz)
nyquist = fs / 2
low = 5 / nyquist
high = 15 / nyquist
b, a = signal.butter(4, [low, high], btype='band')
filtered = signal.filtfilt(b, a, ecg.data)
# Derivative
diff = np.diff(filtered)
diff = np.concatenate([[0], diff])
# Squaring
squared = diff ** 2
# Moving average (integration window ~150ms)
window = int(0.15 * fs)
integrated = uniform_filter1d(squared, size=window)
# Find peaks
if min_distance is None:
min_distance = int(0.4 * fs) # Minimum 40 BPM
peaks, properties = signal.find_peaks(
integrated,
height=threshold * np.max(integrated),
distance=min_distance
)
return peaks
def calculate_heart_rate(
r_peaks: np.ndarray,
sampling_rate: float
) -> float:
"""Calculate average heart rate from R-peaks."""
if len(r_peaks) < 2:
return 0
rr_intervals = np.diff(r_peaks) / sampling_rate # in seconds
mean_rr = np.mean(rr_intervals)
return 60 / mean_rr
def detect_qrs_complex(
ecg: ECGSignal,
r_peaks: np.ndarray
) -> dict:
"""Extract QRS complex characteristics."""
window_before = int(0.1 * ecg.sampling_rate)
window_after = int(0.15 * ecg.sampling_rate)
qrs_data = []
qrs_widths = []
qrs_amplitudes = []
for r_peak in r_peaks:
start = max(0, r_peak - window_before)
end = min(len(ecg.data), r_peak + window_after)
segment = ecg.data[start:end]
qrs_data.append(segment)
# QRS width (derivative-based)
diff = np.abs(np.diff(segment))
qrs_width = np.argmax(np.cumsum(diff) > 0.5 * np.sum(diff))
qrs_widths.append(qrs_width / ecg.sampling_rate * 1000) # ms
# R-peak amplitude
qrs_amplitudes.append(ecg.data[r_peak])
return {
"qrs_widths_ms": np.mean(qrs_widths),
"r_amplitudes_mean": np.mean(qrs_amplitudes),
"beats": len(r_peaks)
}
def detect_arrhythmia(
r_peaks: np.ndarray,
sampling_rate: float
) -> list:
"""Detect potential arrhythmias from RR intervals."""
if len(r_peaks) < 3:
return []
rr_intervals = np.diff(r_peaks) / sampling_rate
rr_diff = np.diff(rr_intervals)
arrhythmias = []
# Tachycardia (>100 bpm)
hr = 60 / rr_intervals
if np.mean(hr) > 100:
arrhythmias.append({
"type": "tachycardia",
"severity": "warning",
"value": np.mean(hr)
})
# Bradycardia (<60 bpm)
if np.mean(hr) < 60:
arrhythmias.append({
"type": "bradycardia",
"severity": "warning",
"value": np.mean(hr)
})
# Irregular rhythm (high RR variability)
rr_std = np.std(rr_intervals)
if rr_std > 0.1: # >100ms variability
arrhythmias.append({
"type": "irregular_rhythm",
"severity": "warning",
"rr_variability_ms": rr_std * 1000
})
# PVC detection (abnormally short RR)
pvc_indices = np.where(rr_intervals[:-1] < 0.8 * np.median(rr_intervals))[0]
if len(pvc_indices) > 0:
arrhythmias.append({
"type": "pvc",
"count": len(pvc_indices),
"severity": "moderate"
})
return arrhythmias
# Example: ECG analysis
ecg = ECGSignal(
data=np.load("ecg_sample.npy"),
sampling_rate=500,
patient_id="P001"
)
r_peaks = detect_r_peaks(ecg)
hr = calculate_heart_rate(r_peaks, 500)
qrs = detect_qrs_complex(ecg, r_peaks)
arrhythmias = detect_arrhythmia(r_peaks, 500)
print(f"Heart Rate: {hr:.1f} bpm")
print(f"QRS Width: {qrs['qrs_widths_ms']:.1f} ms")
Medical Imaging Utilities
import numpy as np
from scipy import ndimage
from skimage import exposure, filters
def apply_window_level(
image: np.ndarray,
window_center: float,
window_width: float
) -> np.ndarray:
"""Apply window/level adjustment for medical imaging."""
min_val = window_center - window_width / 2
max_val = window_center + window_width / 2
windowed = np.clip(image, min_val, max_val)
windowed = (windowed - min_val) / (max_val - min_val)
return windowed
def lung_segmentation(
ct_slice: np.ndarray
) -> np.ndarray:
"""Simple lung segmentation using thresholding."""
# Typical HU values: air <-400, lung -400 to -900, tissue >0
lung_mask = (ct_slice < -200) & (ct_slice > -1000)
# Remove noise
lung_mask = ndimage.binary_opening(lung_mask, iterations=2)
# Fill holes
lung_mask = ndimage.binary_closing(lung_mask, iterations=3)
# Label and keep two largest regions (left and right lung)
labels, num_features = ndimage.label(lung_mask)
if num_features >= 2:
sizes = ndimage.sum(lung_mask, labels, range(1, num_features + 1))
keep = np.argsort(sizes)[-2:] + 1
lung_mask = np.isin(labels, keep)
return lung_mask.astype(np.uint8)
def enhance_contrast(
image: np.ndarray,
method: str = "clahe"
) -> np.ndarray:
"""Enhance contrast in medical images."""
if method == "clahe":
return exposure.equalize_adapthist(image / np.max(image))
elif method == "histogram":
return exposure.equalize_hist(image)
elif method == "rescale":
return exposure.rescale_intensity(image, out_range=(0, 1))
return image
def calculate_hu_statistics(
ct_image: np.ndarray,
mask: np.ndarray = None
) -> dict:
"""Calculate Hounsfield Unit statistics within a region."""
if mask is None:
mask = np.ones_like(ct_image, dtype=bool)
hu_values = ct_image[mask]
return {
"mean_hu": np.mean(hu_values),
"std_hu": np.std(hu_values),
"min_hu": np.min(hu_values),
"max_hu": np.max(hu_values),
"median_hu": np.median(hu_values),
"volume_voxels": np.sum(mask)
}
Biomechanics Calculations
from dataclasses import dataclass
from typing import Tuple
@dataclass
class ForceData:
force_x: np.ndarray
force_y: np.ndarray
force_z: np.ndarray
sampling_rate: float
@dataclass
class BiomechanicalModel:
mass_kg: float
height_m: float
leg_length_m: float
def calculate_center_of_pressure(
force_data: ForceData
) -> Tuple[np.ndarray, np.ndarray]:
"""Calculate COP from ground reaction forces."""
fx, fy, fz = force_data.force_x, force_data.force_y, force_data.force_z
sampling_rate = force_data.sampling_rate
# COP calculation (assuming force plate at origin)
cop_x = -fy / fz
cop_y = -fx / fz
return cop_x, cop_y
def gait_analysis(
cop_x: np.ndarray,
cop_y: np.ndarray,
sampling_rate: float
) -> dict:
"""Analyze gait from COP data."""
# Find gait cycles (peaks in medial-lateral direction)
peaks, _ = signal.find_peaks(cop_x, distance=int(0.5 * sampling_rate))
if len(peaks) < 2:
return {"error": "Insufficient data for gait analysis"}
# Calculate cadence
step_times = np.diff(peaks) / sampling_rate
cadence = 60 / np.mean(step_times) # steps per minute
# Calculate stride length
stride_lengths = np.abs(np.diff(cop_y[peaks]))
stride_length_mean = np.mean(stride_lengths)
# COP velocity
velocity = np.gradient(cop_y) * sampling_rate
velocity_rms = np.sqrt(np.mean(velocity ** 2))
return {
"cadence_steps_min": cadence,
"stride_length_m": stride_length_mean,
"cop_velocity_m_s": velocity_rms,
"num_steps": len(peaks)
}
def calculate_joint_moment(
force: np.ndarray,
moment_arm: np.ndarray
) -> np.ndarray:
"""Calculate joint moment from force and moment arm."""
return np.cross(moment_arm, force)
def bone_stress_analysis(
force: np.ndarray,
cross_sectional_area: float,
section_modulus: float
) -> dict:
"""Calculate stress in bone under load."""
normal_stress = force / cross_sectional_area
bending_stress = moment / section_modulus
return {
"normal_stress": normal_stress,
"bending_stress": bending_stress
}
Medical Device Regulatory Compliance
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import List
class RiskClass(Enum):
CLASS_I = "Class I"
CLASS_II = "Class II"
CLASS_III = "Class III"
@dataclass
class DesignControl:
design_input: str
design_output: str
design_review: str
verification: str
validation: str
@dataclass
class DHFEntry:
document_id: str
title: str
date: datetime
author: str
reviewer: str
status: str
class DesignHistoryFile:
def __init__(self, device_name: str, risk_class: RiskClass):
self.device_name = device_name
self.risk_class = risk_class
self.entries: List[DHFEntry] = []
self.design_controls: List[DesignControl] = []
def add_entry(self, entry: DHFEntry):
self.entries.append(entry)
def add_design_control(self, control: DesignControl):
self.design_controls.append(control)
def generate_compliance_report(self) -> dict:
"""Generate FDA design control compliance report."""
return {
"device_name": self.device_name,
"risk_class": self.risk_class.value,
"total_entries": len(self.entries),
"design_controls_complete": len(self.design_controls),
"required_controls": self._get_required_controls(),
"compliance_status": self._check_compliance()
}
def _get_required_controls(self) -> List[str]:
if self.risk_class == RiskClass.CLASS_I:
return ["design_controls", "establish_registration"]
elif self.risk_class == RiskClass.CLASS_II:
return ["design_controls", "special_controls", "establish_registration", "pmc"]
else:
return ["design_controls", "premarket_approval", "establish_registration", "pmc"]
def _check_compliance(self) -> str:
required = self._get_required_controls()
return "COMPLIANT" if len(self.entries) >= 10 else "INCOMPLETE"
# HIPAA Compliance Check
class HIPAAChecklist:
def __init__(self):
self.checks = {
"access_control": False,
"audit_controls": False,
"integrity_controls": False,
"transmission_security": False,
"authentication": False,
"encryption": False,
"data_backup": False,
"disaster_recovery": False
}
def verify_compliance(self) -> dict:
"""Verify HIPAA compliance status."""
score = sum(self.checks.values()) / len(self.checks) * 100
return {
"compliant": score >= 80,
"score_percent": score,
"passed_checks": sum(self.checks.values()),
"total_checks": len(self.checks),
"details": self.checks
}
Best Practices
- Follow FDA design controls (21 CFR Part 820) for medical devices
- Implement comprehensive risk management per ISO 14971
- Ensure HIPAA compliance for all PHI data
- Use validated software development processes (IEC 62304)
- Perform human factors validation for user-facing devices
- Document all design decisions in Design History File
- Implement traceability from requirements to verification
- Use proper biocompatibility testing for implantables
- Follow cybersecurity best practices for connected devices
- Maintain audit trails for all device modifications