ecg-classify's Introduction

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I am Richard Szilagyi

I spend most of my time-solving complex problems through code. I enjoy collaborating with others to build innovative solutions and improve existing ones. Feel free to explore my projects and repositories, and don't hesitate to get in touch if you have any questions or would like to work together on a project.

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👨‍💻 Programmer and problem solver with a drive to create innovative solutions and a commitment to continuous learning.
👨‍🎓 Studying software development at Munster Technological University gaining knowledge and practical experience in various programming languages, software design, and development methodologies.
🔭 I am currently working on several college projects and various personal projects in my free time.
🌱 I am currently deepening my knowledge and skills in Java backend development using Spring Boot, MongoDB and other NoSQL databases, Python data science libraries, and contributing to open source projects.
🤔 I am curious about learning more about cutting-edge technologies like artificial intelligence, data science and blockchain technologies.
⚡ Fun fact: My name is etched on a microchip and sent to Mars onboard the Perseverance rover as part of a mission to explore the red planet.
🇭🇺 From Budapest, Hungary.
🇮🇪 Living in Ireland.

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I cant read the data in feature extraction step at ECG-classify

Hello, thanks for your code it's mean a lot to help my project.

I tried to implement your code with polysomnography data. it's work until I try to implement Model Fitting, I try to solve the problem, but when I check the Feature Extraction step, I found the code cant process the data, the plot in 'if not' didn't show, here the code I mean

lst = list()

for i in DS1:
rec_index = i

# Tweak the use_filter param
lst = extract_features("slpdb/" + rec_index, length_qrs, length_stt, ar_order_qrs=3, ar_order_stt=3, use_filter=True)
df = pd.DataFrame(lst)

#I try to print the code but the output is null [ ]

if not df.empty and not df[df["type"] == "VEB"].empty and not df[df["type"] == "N"].empty:
n_pre_rr = df[df["type"] == "N"]["pre-RR"]
veb_pre_rr = df[df["type"] == "VEB"]["pre-RR"]
n_post_rr = df[df["type"] == "N"]["post-RR"]
veb_post_rr = df[df["type"] == "VEB"]["post-RR"]

    fig1 = plt.figure()
    ax1 = fig1.add_subplot(121, title="Record {} | Pre-RR")
    ax1.scatter(n_pre_rr, c="c", label="Normal")
    ax1.scatter(veb_pre_rr, c="r", label="VEB")
    
    

    n_qrs_ar_array = series2arCoeffs(df[df["type"] == "N"]["QRS_ar_coeffs"])
    veb_qrs_ar_array = series2arCoeffs(df[df["type"] == "VEB"]["QRS_ar_coeffs"])
    n_stt_ar_array = series2arCoeffs(df[df["type"] == "N"]["ST/T_ar_coeffs"])
    veb_stt_ar_array = series2arCoeffs(df[df["type"] == "VEB"]["ST/T_ar_coeffs"])

    fig = plt.figure(figsize=(20, 5))

    ax1 = fig.add_subplot(141, title="Record {} | QRS | a0 vs a1".format(rec_index))
    plt.ylim(-2.5, 0.5)
    plt.xlim(0.8, 2.6)
    ax1.scatter(x=n_qrs_ar_array[:, 0], y=n_qrs_ar_array[:, 1], c='c', label="Normal")
    ax1.scatter(x=veb_qrs_ar_array[:, 0], y=veb_qrs_ar_array[:, 1], c='r', label="VEB")
    plt.legend(loc="best")

    ax2 = fig.add_subplot(142, title="Record {} | QRS | a1 vs a2".format(rec_index))
    plt.ylim(-0.6, 0.8)
    plt.xlim(-2.5, 0.5)
    ax2.scatter(x=n_qrs_ar_array[:, 1], y=n_qrs_ar_array[:, 2], c='c', label="Normal")
    ax2.scatter(x=veb_qrs_ar_array[:, 1], y=veb_qrs_ar_array[:, 2], c='r', label="VEB")
    plt.legend(loc="best")

    ax3 = fig.add_subplot(143, title="Record {} | ST/T | a0 vs a1".format(rec_index))
    plt.ylim(-3, 0.5)
    plt.xlim(0.5, 3)
    ax3.scatter(x=n_stt_ar_array[:, 0], y=n_stt_ar_array[:, 1], c='c', label="Normal")
    ax3.scatter(x=veb_stt_ar_array[:, 0], y=veb_stt_ar_array[:, 1], c='r', label="VEB")
    plt.legend(loc="best")

    ax4 = fig.add_subplot(144, title="Record {} | ST/T | a1 vs a2".format(rec_index))
    plt.ylim(-0.6, 1)
    plt.xlim(-3, 0.5)
    ax4.scatter(x=n_stt_ar_array[:, 1], y=n_stt_ar_array[:, 2], c='c', label="Normal")
    ax4.scatter(x=veb_stt_ar_array[:, 1], y=veb_stt_ar_array[:, 2], c='r', label="VEB")
    plt.legend(loc="best")

else:
    print("Record {} : At least one empty category : VEB or N.".format(rec_index)

What should I do to solve the code?

Thanks for your help

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ecg-classify's Introduction

Hello there

I am Richard Szilagyi

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ecg-classify's Issues

I cant read the data in feature extraction step at ECG-classify

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