Name: Biomedical Engineering
Author: ffsshhttiikk

What I do

Design medical devices and diagnostic equipment
Analyze physiological systems and model biological processes
Develop biomedical imaging systems and signal processing
Create prosthetic devices and rehabilitation technology
Apply engineering principles to healthcare solutions

When to use me

When developing medical devices or healthcare technology
When analyzing biological signals (ECG, EEG, EMG)
When designing prosthetic or orthotic devices
When working on diagnostic equipment or imaging systems
When modeling physiological processes

Key Concepts

Biomedical Signals

import numpy as np
from scipy import signal

# ECG processing example
def filter_ecg(raw_signal, fs=1000):
    # Remove powerline interference
    b, a = signal.iirnotch(60, 30, fs)
    filtered = signal.filtfilt(b, a, raw_signal)
    
    # Bandpass filter for QRS detection
    low, high = 5, 15
    b, a = signal.butter(4, [low, high], btype='bandpass', fs=fs)
    return signal.filtfilt(b, a, filtered)

# Heart rate calculation
def calculate_bpm(rr_intervals):
    return 60000 / np.mean(rr_intervals)

What I do

Design medical devices and diagnostic equipment
Analyze physiological systems and model biological processes
Develop biomedical imaging systems and signal processing
Create prosthetic devices and rehabilitation technology
Apply engineering principles to healthcare solutions

When to use me

When developing medical devices or healthcare technology
When analyzing biological signals (ECG, EEG, EMG)
When designing prosthetic or orthotic devices
When working on diagnostic equipment or imaging systems
When modeling physiological processes

Key Concepts

Biomedical Signals

import numpy as np
from scipy import signal

# ECG processing example
def filter_ecg(raw_signal, fs=1000):
    # Remove powerline interference
    b, a = signal.iirnotch(60, 30, fs)
    filtered = signal.filtfilt(b, a, raw_signal)
    
    # Bandpass filter for QRS detection
    low, high = 5, 15
    b, a = signal.butter(4, [low, high], btype='bandpass', fs=fs)
    return signal.filtfilt(b, a, filtered)

# Heart rate calculation
def calculate_bpm(rr_intervals):
    return 60000 / np.mean(rr_intervals)

Modality	Principle	Applications
X-ray	X-ray attenuation	Bone, chest imaging
CT	X-ray tomography	3D anatomy
MRI	Nuclear magnetic resonance	Soft tissue imaging
Ultrasound	Echo reflection	Obstetrics, cardiology
PET	Radioactive decay	Functional imaging

Biomedical Engineering

What I do

When to use me

Key Concepts

Biomedical Signals

Biomedical Engineering

What I do

When to use me

Key Concepts

Biomedical Signals

Medical Imaging Modalities

Biomechanics

Device Design Considerations

Regulatory Framework

Sessions

Docker Patterns

Autonomous Loops

Kotlin Patterns

Eval Harness

Golang Patterns